diff --git a/.DS_Store b/.DS_Store
deleted file mode 100644
index a8bebcf..0000000
Binary files a/.DS_Store and /dev/null differ
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
new file mode 100644
index 0000000..5fc3cab
--- /dev/null
+++ b/.github/workflows/ci.yml
@@ -0,0 +1,69 @@
+name: CI
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+
+permissions:
+  contents: read
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.9", "3.11"]
+
+    steps:
+      - uses: actions/checkout@v7
+      - uses: actions/setup-python@v6
+        with:
+          python-version: ${{ matrix.python-version }}
+          cache: pip
+      - name: Install ALDes and dependencies
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install -e ".[test]"
+      - name: Verify installed commands and dependencies
+        run: |
+          python -m pip check
+          aldes-train --help
+          aldes-paper-subset --help
+      - name: Lint
+        run: |
+          python -m ruff check .
+          python -m ruff format --check .
+      - name: Test
+        env:
+          ALDES_DEVICE: cpu
+          ALDES_EVAL_WORKERS: "2"
+        run: python -m pytest -q
+
+  package:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v7
+      - uses: actions/setup-python@v6
+        with:
+          python-version: "3.11"
+          cache: pip
+      - run: python -m pip install --upgrade pip build twine
+      - run: python -m build
+      - run: python -m twine check dist/*
+      - run: python -m venv /tmp/aldes-wheel
+      - run: /tmp/aldes-wheel/bin/python -m pip install dist/*.whl
+      - name: Validate the installed wheel outside the repository
+        working-directory: /tmp
+        run: |
+          /tmp/aldes-wheel/bin/python -m pip check
+          /tmp/aldes-wheel/bin/aldes-train --help
+          /tmp/aldes-wheel/bin/aldes-paper-subset --help
+          /tmp/aldes-wheel/bin/python - <<'PY'
+          import importlib.metadata
+          from run_paper_subset import DEFAULT_REFERENCE_DIR, _reference_result
+
+          assert importlib.metadata.version("aldes") == "2.0.0"
+          assert DEFAULT_REFERENCE_DIR.is_dir()
+          assert _reference_result(DEFAULT_REFERENCE_DIR, 1).size == 30
+          PY
diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
new file mode 100644
index 0000000..74fe5f8
--- /dev/null
+++ b/.github/workflows/release.yml
@@ -0,0 +1,26 @@
+name: release assets
+
+on:
+  release:
+    types: [published]
+
+permissions:
+  contents: write
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v7
+        with:
+          ref: ${{ github.event.release.tag_name }}
+      - uses: actions/setup-python@v6
+        with:
+          python-version: "3.11"
+      - run: python -m pip install --upgrade pip build twine
+      - run: python -m build
+      - run: python -m twine check dist/*
+      - name: Attach distributions to the GitHub release
+        env:
+          GH_TOKEN: ${{ github.token }}
+        run: gh release upload "${{ github.event.release.tag_name }}" dist/* --clobber
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..f2ab8f0
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,15 @@
+.DS_Store
+__pycache__/
+*.py[cod]
+.pytest_cache/
+.ruff_cache/
+.venv/
+build/
+dist/
+*.egg-info/
+logs/
+experiments/
+ela/
+*.pt
+*.pth
+*.ckpt
diff --git a/CHANGELOG.md b/CHANGELOG.md
new file mode 100644
index 0000000..e179a42
--- /dev/null
+++ b/CHANGELOG.md
@@ -0,0 +1,43 @@
+# Changelog
+
+All notable changes to ALDes are documented here.
+
+## 2.0.0 - 2026-07-20
+
+### Added
+
+- A pure-Python execution path backed by AutoOptLib 1.3.0, with no MATLAB or
+  MATLAB Engine runtime dependency.
+- Automatic PyTorch accelerator selection for NVIDIA CUDA, AMD ROCm, Apple
+  MPS, and CPU, with environment-variable overrides.
+- Deterministic CPU multiprocessing for generated-algorithm and objective
+  evaluation while neural-network work remains on the selected PyTorch device.
+- Explicit independent single-problem and feature-conditioned continual-design
+  modes. Independent design is the default and does not extract problem
+  features; continual design uses landscape features and EWC.
+- A constrained generator and matching executor grammar: fork can follow
+  choose, each branch contains one search operation except that crossover may
+  be followed by mutation, and branches merge into one shared update.
+- Command-line entry points for training and the time-bounded paper subset,
+  portable packaged reference results, tests, and cross-version CI.
+
+### Changed
+
+- Aligned the PBO training protocol with the paper: 100 PPO epochs, 16 sampled
+  algorithms per epoch, five PPO updates, 5,000 training evaluations, and an
+  optional 30-run/50,000-evaluation final test.
+- Replaced the historical Python-to-MATLAB bridge with the public Python
+  AutoOptLib ALDes backend and its 32-token vocabulary.
+- Made one PBO problem and one seed the safe default command-line scope.
+- Moved historical plotting results from the removed MATLAB tree into
+  `draw/datas/reference_results` without changing their contents.
+
+### Removed
+
+- Bundled MATLAB source, MATLAB-specific bridge scripts, stale bytecode,
+  obsolete TorchText data loaders, and generated experiment artifacts.
+
+## 1.0.0
+
+- Original research release combining Python model training with MATLAB
+  algorithm execution.
diff --git a/CITATION.cff b/CITATION.cff
new file mode 100644
index 0000000..5b0ab14
--- /dev/null
+++ b/CITATION.cff
@@ -0,0 +1,40 @@
+cff-version: 1.2.0
+message: "If you use ALDes, please cite the paper below."
+title: "ALDes: Automated Metaheuristic Algorithm Design with Autoregressive Learning"
+type: software
+version: 2.0.0
+date-released: 2026-07-20
+repository-code: "https://github.com/auto4opt/ALDes"
+license: Apache-2.0
+authors:
+  - family-names: Zhao
+    given-names: Qi
+  - family-names: Liu
+    given-names: Tengfei
+  - family-names: Yan
+    given-names: Bai
+  - family-names: Duan
+    given-names: Qiqi
+  - family-names: Yang
+    given-names: Jian
+  - family-names: Shi
+    given-names: Yuhui
+preferred-citation:
+  type: article
+  title: "Automated Metaheuristic Algorithm Design with Autoregressive Learning"
+  year: 2024
+  doi: "10.1109/TEVC.2024.3464677"
+  journal: "IEEE Transactions on Evolutionary Computation"
+  authors:
+    - family-names: Zhao
+      given-names: Qi
+    - family-names: Liu
+      given-names: Tengfei
+    - family-names: Yan
+      given-names: Bai
+    - family-names: Duan
+      given-names: Qiqi
+    - family-names: Yang
+      given-names: Jian
+    - family-names: Shi
+      given-names: Yuhui
diff --git a/EWC.py b/EWC.py
index 727521e..5e3759f 100644
--- a/EWC.py
+++ b/EWC.py
@@ -1,60 +1,40 @@
-from copy import deepcopy
+"""Diagonal-Fisher elastic weight consolidation for continual ALDes."""
+
+from __future__ import annotations
 
 import torch
 from torch import nn
-from torch.nn import functional as F
-from torch.autograd import Variable
-import torch.utils.data
-
-
-def variable(t: torch.Tensor, use_cuda=True, **kwargs):
-    if torch.cuda.is_available() and use_cuda:
-        t = t.cuda()
-    return Variable(t, **kwargs)
 
 
-class EWC(object):
+class EWC:
     def __init__(self, model: nn.Module):
-
-        self.model = model
-
-        self.params = {n: p for n, p in self.model.named_parameters() if p.requires_grad}
-        self._means = {}
-        self._precision_matrices = None #self._diag_fisher()
-
-        for n, p in deepcopy(self.params).items():
-            self._means[n] = variable(p.data)
-
-    def _diag_fisher(self):
-        precision_matrices = {}
-        for n, p in deepcopy(self.params).items():
-            p.data.zero_()
-            precision_matrices[n] = variable(p.data)
-
-        #self.model.eval()
-        for n, p in self.model.named_parameters():
-            if p.grad != None:
-                precision_matrices[n].data += p.grad.data ** 2
-        precision_matrices = {n: p for n, p in precision_matrices.items()}
-        return precision_matrices
-
-    def update_diag_fisher(self,model):
-        precision_matrices = {}
-        for n, p in deepcopy(self.params).items():
-            p.data.zero_()
-            precision_matrices[n] = variable(p.data)
-        for n, p in model.named_parameters():
-            if p.grad != None:
-                precision_matrices[n].data += p.grad.data ** 2
-        precision_matrices = {n: p for n, p in precision_matrices.items()}
-        if self._precision_matrices is None:
-            self._precision_matrices = precision_matrices
-        else:
-            for key in precision_matrices:
-                self._precision_matrices[key] +=precision_matrices[key]
-    def penalty(self, model: nn.Module):
-        loss = 0
-        for n, p in model.named_parameters():
-            _loss = self._precision_matrices[n] * (p - self._means[n]) ** 2
-            loss += _loss.sum()
-        return loss*100
\ No newline at end of file
+        self._means = {
+            name: parameter.detach().clone()
+            for name, parameter in model.named_parameters()
+            if parameter.requires_grad
+        }
+        self._precision_matrices = {
+            name: torch.zeros_like(parameter)
+            for name, parameter in model.named_parameters()
+            if parameter.requires_grad
+        }
+
+    def update_diag_fisher(self, model: nn.Module) -> None:
+        """Accumulate squared policy gradients for one sampled batch."""
+
+        for name, parameter in model.named_parameters():
+            if name in self._precision_matrices and parameter.grad is not None:
+                self._precision_matrices[name] += parameter.grad.detach().square()
+
+    def penalty(self, model: nn.Module) -> torch.Tensor:
+        loss = torch.zeros((), device=next(model.parameters()).device)
+        for name, parameter in model.named_parameters():
+            if name in self._precision_matrices:
+                loss = (
+                    loss
+                    + (
+                        self._precision_matrices[name]
+                        * (parameter - self._means[name]).square()
+                    ).sum()
+                )
+        return loss
diff --git a/README.md b/README.md
index fe17dcb..222af0f 100644
--- a/README.md
+++ b/README.md
@@ -1,13 +1,191 @@
-Code for Automated Metaheuristic Algorithm Design with Autoregressive Learning
+# ALDes
 
-### 1. Env congfig：
-  
-  Follow aldes.yaml, and you need congfig Matlab R2020B connect to you aldes python env.
-### 2. Training:  
+Pure-Python implementation of **Automated Metaheuristic Algorithm Design with
+Autoregressive Learning** (ALDes).
 
-#### Prepare
-  Run plfacco_feature.py to calculate PBO problem's Landscape feature.
+ALDes treats algorithm design as constrained autoregressive sequence generation.
+A Transformer policy generates a variable-length metaheuristic program, PPO
+learns from the program's performance, and AutoOptLib executes the generated
+algorithm without MATLAB or MATLAB Engine.
 
-#### Training
-  In train.py, run rain_separately() for single problem task, and train_in_one() for continual problem task.
+## Release scope
 
+This release supports the paper's 23 pseudo-Boolean optimization (PBO) tasks:
+
+- independent design from scratch for one target problem;
+- feature-conditioned continual design with EWC;
+- the paper's training and test budgets;
+- automatic neural-network acceleration on CUDA, ROCm, or Apple MPS;
+- parallel CPU evaluation of generated algorithms.
+
+The paper's RIS beamforming and power-system restoration experiments are not
+part of this release. The repository therefore makes no claim that those two
+application results can be reproduced here.
+
+## Installation
+
+Python 3.9--3.11 is supported. Clone only this repository and install it from
+the repository root:
+
+```bash
+git clone --branch v2.0.0 --depth 1 https://github.com/auto4opt/ALDes.git
+cd ALDes
+python -m pip install -e .
+```
+
+The installation automatically downloads the compatible
+`autooptlib[aldes]` dependency from the official AutoOptLib GitHub release.
+Users do not need to clone AutoOptLib separately. The Python import name is
+lowercase:
+
+```python
+import autooptlib
+```
+
+A Conda environment can be created instead:
+
+```bash
+conda env create -f aldes.yaml
+conda activate aldes
+```
+
+For development and tests, install the test extra:
+
+```bash
+python -m pip install -e ".[test]"
+```
+
+## Quick start
+
+The default command performs one independent design trial on PBO F1 with
+seed 1:
+
+```bash
+aldes-train
+```
+
+The equivalent source command is:
+
+```bash
+python train.py
+```
+
+The inferred algorithm sequence is printed and written to the run log. A
+single-problem run does not save a model checkpoint because the trained policy
+is not reused after its final algorithm has been inferred.
+
+Choose another problem or multiple explicit trials with command-line options:
+
+```bash
+aldes-train --problems 14 --seeds 1
+aldes-train --problems 1,14,15 --seeds 1,2
+aldes-train --problems 14 --seeds 1 --evaluate-test
+```
+
+`--evaluate-test` applies the paper's full 30-run test protocol after training.
+Without it, only training and final algorithm inference are performed.
+
+## Continual design
+
+Continual mode extracts PBO landscape features, conditions one policy on those
+features, and applies EWC between tasks:
+
+```bash
+aldes-train --mode continual --problems 1,2,11 --seeds 1
+```
+
+The default continual sequence is defined in `conf.py`. Checkpoint output is
+optional and is only intended for a policy that must be reused across continual
+stages:
+
+```bash
+aldes-train --mode continual --checkpoint-dir logs/continual
+```
+
+No checkpoint binary is distributed with this repository.
+
+## Compute devices
+
+PyTorch devices are selected automatically in this order:
+
+1. NVIDIA CUDA or AMD ROCm;
+2. Apple Metal Performance Shaders (MPS);
+3. CPU.
+
+PyTorch must have been built for the user's accelerator. In ROCm builds, AMD
+devices are exposed through PyTorch's `cuda` API. Override automatic selection
+when needed:
+
+```bash
+ALDES_DEVICE=cpu aldes-train
+ALDES_DEVICE=cuda aldes-train
+ALDES_DEVICE=mps aldes-train
+```
+
+Only neural-network training and inference use the selected accelerator.
+Generated algorithms and objective functions run on CPUs. Candidate algorithms
+are evaluated in parallel using up to the available logical CPU cores:
+
+```bash
+ALDES_EVAL_WORKERS=8 aldes-train
+ALDES_EVAL_WORKERS=1 aldes-train  # disable multiprocessing
+```
+
+## Paper protocol
+
+The default configuration for each PBO design trial is:
+
+- training instances: dimensions 100, 225, and 400;
+- 100 PPO epochs and 16 generated algorithms per epoch;
+- 5 PPO updates per epoch;
+- 5 runs per training instance and 5,000 function evaluations per run;
+- population size 50;
+- test instance: dimension 625;
+- 30 test runs and 50,000 function evaluations per run.
+
+The single-problem mode does not extract or input problem features. Feature
+conditioning is enabled only in continual mode.
+
+## Time-bounded paper subset
+
+The repository includes a runner that completes whole paper-protocol trials
+until the next problem is predicted to exceed a wall-clock budget:
+
+```bash
+aldes-paper-subset --problems 1,14,15 --time-budget-minutes 60
+```
+
+The budget is checked only between problems. A started problem always retains
+all 100 epochs and the complete 30-run test. Structured JSON results are written
+under `experiments/`; this local output directory and all model checkpoints are
+ignored by Git.
+
+Historical comparison data used by the plotting notebooks are stored under
+`draw/datas/reference_results` and can be read with SciPy.
+
+## Tests
+
+Run the test suite and static checks with:
+
+```bash
+python -m pytest -q
+python -m ruff check .
+```
+
+GitHub Actions repeats installation, linting, and tests on Linux with Python
+3.9 and 3.11. Tests cover device selection, serial/parallel evaluation parity,
+single and continual feature modes, grammar-valid generation, PPO likelihood
+replay, EWC accumulation, and a pure-Python PPO update.
+
+## Citation
+
+If you use ALDes, cite:
+
+> Q. Zhao, T. Liu, B. Yan, Q. Duan, J. Yang, and Y. Shi, "Automated
+> Metaheuristic Algorithm Design with Autoregressive Learning," IEEE
+> Transactions on Evolutionary Computation, 2024.
+> https://doi.org/10.1109/TEVC.2024.3464677
+
+## License
+
+ALDes is released under the Apache License 2.0. See `LICENSE`.
diff --git a/__pycache__/EWC.cpython-38.pyc b/__pycache__/EWC.cpython-38.pyc
deleted file mode 100644
index 39d135f..0000000
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diff --git a/__pycache__/matlab_setting.cpython-38.pyc b/__pycache__/matlab_setting.cpython-38.pyc
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diff --git a/__pycache__/pflacco_test.cpython-38.pyc b/__pycache__/pflacco_test.cpython-38.pyc
deleted file mode 100644
index 930753a..0000000
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diff --git a/aldes.yaml b/aldes.yaml
index ab42c00..c5908d4 100644
--- a/aldes.yaml
+++ b/aldes.yaml
@@ -1,135 +1,8 @@
 name: aldes
 channels:
-  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
-  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
-  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
-  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/
-  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/
-  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/fastai/
-  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/
-  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda/
+  - conda-forge
 dependencies:
-  - ca-certificates=2023.08.22=haa95532_0
-  - openssl=1.1.1w=h2bbff1b_0
-  - pip=23.3.1=py38haa95532_0
-  - python=3.8.0=hff0d562_2
-  - setuptools=68.0.0=py38haa95532_0
-  - sqlite=3.41.2=h2bbff1b_0
-  - vc=14.2=h21ff451_1
-  - vs2015_runtime=14.27.29016=h5e58377_2
-  - wheel=0.41.2=py38haa95532_0
+  - python>=3.9,<3.12
+  - pip>=23
   - pip:
-    - absl-py==2.0.0
-    - aiosignal==1.3.1
-    - asttokens==2.4.1
-    - astunparse==1.6.3
-    - attrs==23.2.0
-    - backcall==0.2.0
-    - cachetools==5.3.2
-    - certifi==2023.11.17
-    - charset-normalizer==3.3.2
-    - click==8.1.7
-    - colorama==0.4.6
-    - comm==0.2.2
-    - contourpy==1.1.1
-    - cycler==0.12.1
-    - debugpy==1.8.2
-    - decorator==5.1.1
-    - dill==0.3.7
-    - einops==0.7.0
-    - et-xmlfile==1.1.0
-    - executing==2.0.1
-    - filelock==3.15.4
-    - flatbuffers==23.5.26
-    - fonttools==4.46.0
-    - frozenlist==1.4.1
-    - gast==0.4.0
-    - google-auth==2.24.0
-    - google-auth-oauthlib==1.0.0
-    - google-pasta==0.2.0
-    - grpcio==1.59.3
-    - h5py==3.10.0
-    - idna==3.6
-    - importlib-metadata==7.0.0
-    - importlib-resources==6.1.1
-    - ioh==0.3.14
-    - ipykernel==6.29.5
-    - ipython==8.12.3
-    - jedi==0.19.1
-    - joblib==1.3.2
-    - jsonschema==4.23.0
-    - jsonschema-specifications==2023.12.1
-    - jupyter-client==8.6.2
-    - jupyter-core==5.7.2
-    - keras==2.13.1
-    - kiwisolver==1.4.5
-    - libclang==16.0.6
-    - markdown==3.5.1
-    - markupsafe==2.1.3
-    - matlabengineforpython==9.13
-    - matplotlib==3.7.4
-    - matplotlib-inline==0.1.7
-    - msgpack==1.0.8
-    - multiprocess==0.70.15
-    - nest-asyncio==1.6.0
-    - numdifftools==0.9.41
-    - numpy==1.24.3
-    - oauthlib==3.2.2
-    - openpyxl==3.1.2
-    - opt-einsum==3.3.0
-    - packaging==23.2
-    - pandas==2.0.3
-    - parso==0.8.4
-    - pflacco==1.2.2
-    - pickleshare==0.7.5
-    - pillow==10.1.0
-    - pkgutil-resolve-name==1.3.10
-    - platformdirs==4.2.2
-    - prompt-toolkit==3.0.47
-    - protobuf==4.25.1
-    - psutil==6.0.0
-    - pure-eval==0.2.2
-    - pyasn1==0.5.1
-    - pyasn1-modules==0.3.0
-    - pydoe==0.3.8
-    - pygments==2.18.0
-    - pyparsing==3.1.1
-    - python-dateutil==2.8.2
-    - pytz==2023.3.post1
-    - pywin32==306
-    - pyyaml==6.0.1
-    - pyzmq==26.0.3
-    - ray==2.10.0
-    - referencing==0.35.1
-    - requests==2.31.0
-    - requests-oauthlib==1.3.1
-    - rpds-py==0.19.0
-    - rsa==4.9
-    - salib==1.4.7
-    - scikit-learn==1.2.2
-    - scipy==1.10.1
-    - seaborn==0.13.2
-    - six==1.16.0
-    - stack-data==0.6.3
-    - tensorboard==2.13.0
-    - tensorboard-data-server==0.7.2
-    - tensorboardx==2.6.2.2
-    - tensorflow==2.13.0
-    - tensorflow-estimator==2.13.0
-    - tensorflow-intel==2.13.0
-    - tensorflow-io-gcs-filesystem==0.31.0
-    - termcolor==2.4.0
-    - threadpoolctl==3.2.0
-    - torch==1.10.0+cu102
-    - torchtext==0.11.0
-    - tornado==6.4.1
-    - tqdm==4.66.1
-    - traitlets==5.14.3
-    - typing-extensions==4.5.0
-    - tzdata==2023.3
-    - urllib3==2.1.0
-    - wcwidth==0.2.13
-    - werkzeug==3.0.1
-    - wrapt==1.16.0
-    - zipp==3.17.0
-prefix: C:\Users\21902\.conda\envs\aldes
+      - -e .[test]
diff --git a/aldes_setting.py b/aldes_setting.py
new file mode 100644
index 0000000..2f743e0
--- /dev/null
+++ b/aldes_setting.py
@@ -0,0 +1,9 @@
+"""Compatibility aliases for the original standalone ALDes model."""
+
+from autooptlib.aldes.vocabulary import BEGIN_INDEX, END_INDEX, VOCABULARY_SIZE
+
+begin_index = BEGIN_INDEX
+end_index = END_INDEX
+total_index = VOCABULARY_SIZE
+
+__all__ = ["begin_index", "end_index", "total_index"]
diff --git a/conf.py b/conf.py
index 46b6e00..c1aced3 100644
--- a/conf.py
+++ b/conf.py
@@ -1,10 +1,13 @@
-import torch
+import os
 
-# GPU device setting
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+from util.device import resolve_device
 
-# model parameter setting
-batch_size = 1
+# Automatically prefer NVIDIA CUDA / AMD ROCm, then Apple MPS, and finally
+# CPU. Set ALDES_DEVICE=cpu/cuda/mps/rocm to override automatic selection.
+device_preference = os.environ.get("ALDES_DEVICE", "auto")
+device = resolve_device(device_preference)
+
+# Transformer architecture
 max_len = 50
 d_model = 32
 n_layers = 8
@@ -12,22 +15,22 @@
 ffn_hidden = 2048
 drop_prob = 0.1
 
-# optimizer parameter setting
+# ALDes has two explicit modes. Single-problem design is the default and does
+# not extract or feed landscape features. Continual design conditions the
+# policy on paper-style PBO features and may use EWC between tasks.
+aldes_mode = "single"  # "single" or "continual"
+use_ewc = True
+ewc_weight = 200.0
+continual_problem_sets = (
+    (1, 2, 3, 4, 5, 6, 7),
+    (1, 2, 11, 18, 19, 22, 23),
+)
+
+# Optimizer and PPO settings
 init_lr = 5e-5
-factor = 0.9
 adam_eps = 5e-9
-patience = 10
-warmup = 100
-epoch = 1000
 clip = 1.0
 weight_decay = 5e-4
-inf = float('inf')
-
-# my config
-total_epoch = 50 # DEFULT 100
+total_epoch = 100
 ppo_epoch = 5
 batch_size_src = 16
-
-model_type = 0 # 0 :transformer 1: LSTM
-train_type = 0 # 0 :PPO 1: PG
-
diff --git a/data.py b/data.py
deleted file mode 100644
index 45780de..0000000
--- a/data.py
+++ /dev/null
@@ -1,10 +0,0 @@
-from conf import *
-from util.data_loader import DataLoader
-from util.tokenizer import Tokenizer
-
-
-src_pad_idx = None
-trg_pad_idx = None
-trg_sos_idx = None
-enc_voc_size = 100
-dec_voc_size = 30
\ No newline at end of file
diff --git a/draw/__init__.py b/draw/__init__.py
new file mode 100644
index 0000000..cca2518
--- /dev/null
+++ b/draw/__init__.py
@@ -0,0 +1 @@
+"""Plotting notebooks and packaged ALDes reference results."""
diff --git a/draw/bloxplot.ipynb b/draw/bloxplot.ipynb
index 953c108..0c04fcb 100644
--- a/draw/bloxplot.ipynb
+++ b/draw/bloxplot.ipynb
@@ -32,12 +32,12 @@
     "        datas = []\n",
     "        for alg in algs:\n",
     "            if alg == 'tabu_irace':\n",
-    "                pkl_file_path = f'../matlab/result/{alg}/instance_4/' + f'f{problem_id}.pkl'\n",
+    "                pkl_file_path = f'datas/reference_results/{alg}/instance_4/' + f'f{problem_id}.pkl'\n",
     "                with open(pkl_file_path, 'rb') as f:\n",
     "                    # loaded_data: steps(100)*algs(16)*instance(3)*runs(5)\n",
     "                    data = pickle.load(f)\n",
     "            else: \n",
-    "                mat_file_path = f'../matlab/result/{alg}/instance_4/' + f'f{problem_id}.mat'\n",
+    "                mat_file_path = f'datas/reference_results/{alg}/instance_4/' + f'f{problem_id}.mat'\n",
     "                mat_data = scipy.io.loadmat(mat_file_path)\n",
     "                data = -mat_data['res'][0]\n",
     "            datas.append(data)\n",
@@ -64,7 +64,7 @@
     "    datas = []\n",
     "    for problem_id in [1,13,15,20]:\n",
     "        \n",
-    "        mat_file_path = f'../matlab/result/design/instance_4/' + f'f{problem_id}.mat'\n",
+    "        mat_file_path = f'datas/reference_results/design/instance_4/' + f'f{problem_id}.mat'\n",
     "        mat_data = scipy.io.loadmat(mat_file_path)\n",
     "\n",
     "        data = -mat_data['res'][0]\n",
@@ -115,7 +115,7 @@
    "source": [
     "algs = ['design','Discrete_Genetic_Algorithm','Discrete_Iterative_Local_Search','tabu_irace']\n",
     "display_alg_name = ['Ours', 'GA', 'ILS', 'TS']\n",
-    "save_path = f'result_pictures/bloxplot/'\n",
+    "save_path = 'paper_pictures/Supplementary_Pic/'\n",
     "\n",
     "#draw_bloxplot(algs, display_alg_name, save_path)\n",
     "draw_bloxplot_in_one_pic(save_path)"
diff --git a/draw/continue_problem.ipynb b/draw/continue_problem.ipynb
index 417c2c6..fe4aabb 100644
--- a/draw/continue_problem.ipynb
+++ b/draw/continue_problem.ipynb
@@ -1105,7 +1105,7 @@
     "    # plt.xlabel('Episode')\n",
     "    # plt.ylabel('Performance')\n",
     "\n",
-    "    plt.savefig(f'result_pictures/continue_problem\\F{problem}.svg',dpi=300,format=\"svg\",bbox_inches = 'tight')"
+    "    plt.savefig(f'paper_pictures/continue_problem/F{problem}.svg',dpi=300,format=\"svg\",bbox_inches = 'tight')"
    ]
   },
   {
diff --git a/draw/convergence_curve.ipynb b/draw/convergence_curve.ipynb
index 9dcd6bc..a4c553c 100644
--- a/draw/convergence_curve.ipynb
+++ b/draw/convergence_curve.ipynb
@@ -69,7 +69,7 @@
     "# draw convergence curves of algs in paper with Pseudocode [1,13,15,20]\n",
     "problem_set = [1, 13, 15, 20]\n",
     "\n",
-    "data_path = 'D:\\\\01Code\\\\ALDes\\\\draw\\\\datas\\\\mats\\\\ArchSolution\\\\'\n",
+    "data_path = 'datas/mats/ArchSolution/'\n",
     "for problem in problem_set:\n",
     "    mat_path = data_path + f'f{problem}.mat'\n",
     "    mat_data = scipy.io.loadmat(mat_path)\n",
@@ -81,7 +81,7 @@
     "    plt.xlabel('Iterations')\n",
     "    plt.ylabel('Performance')\n",
     "\n",
-    "    plt.savefig(f'result_pictures\\convergence_curve\\Alg{problem}.svg',dpi=300,format=\"svg\",bbox_inches = 'tight')\n",
+    "    plt.savefig(f'paper_pictures/Supplementary_Pic/convergence_curve/Alg{problem}.svg',dpi=300,format=\"svg\",bbox_inches = 'tight')\n",
     "\n"
    ]
   }
diff --git a/draw/datas/__init__.py b/draw/datas/__init__.py
new file mode 100644
index 0000000..eadbc07
--- /dev/null
+++ b/draw/datas/__init__.py
@@ -0,0 +1 @@
+"""Data resources used by the ALDes plotting notebooks."""
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-      "result_pictures/diff_FE_fig3/F15.svg\n",
-      "result_pictures/diff_FE_fig3/F17.svg\n",
-      "result_pictures/diff_FE_fig3/F20.svg\n"
+      "paper_pictures/Supplementary_Pic/diff_FE_fig3/F1.svg\n",
+      "paper_pictures/Supplementary_Pic/diff_FE_fig3/F3.svg\n",
+      "paper_pictures/Supplementary_Pic/diff_FE_fig3/F14.svg\n",
+      "paper_pictures/Supplementary_Pic/diff_FE_fig3/F15.svg\n",
+      "paper_pictures/Supplementary_Pic/diff_FE_fig3/F17.svg\n",
+      "paper_pictures/Supplementary_Pic/diff_FE_fig3/F20.svg\n"
      ]
     },
     {
@@ -237,9 +237,9 @@
     "dfs = [FE_5000, FE_3000 ,FE_10000]\n",
     "display_name = ['FE5000','FE3000','FE10000']\n",
     "problem_set = [1,3,14,15,17,20]\n",
-    "save_path = f'result_pictures/diff_FE_fig3/'\n",
+    "save_path = 'paper_pictures/Supplementary_Pic/diff_FE_fig3/'\n",
     "\n",
-    "roll_and_draw_mutil(dfs,problem_set,display_name,save_path)"
+    "roll_and_draw_multiple(dfs,problem_set,display_name,save_path)"
    ]
   }
  ],
diff --git a/draw/single_probelm.ipynb b/draw/single_probelm.ipynb
index 15feb17..1cb89b8 100644
--- a/draw/single_probelm.ipynb
+++ b/draw/single_probelm.ipynb
@@ -1062,9 +1062,9 @@
     "    plt.xlabel('Episode')\n",
     "    plt.ylabel('Performance')\n",
     "    if show_std:\n",
-    "        plt.savefig(f'result_pictures/single_problem_with_std\\F{problem}.svg',dpi=300,format=\"svg\",bbox_inches = 'tight')\n",
+    "        plt.savefig(f'paper_pictures/single_problem_with_std/F{problem}.svg',dpi=300,format=\"svg\",bbox_inches = 'tight')\n",
     "    else:\n",
-    "        plt.savefig(f'result_pictures/single_problem\\F{problem}.svg',dpi=300,format=\"svg\",bbox_inches = 'tight')"
+    "        plt.savefig(f'paper_pictures/single_problem/F{problem}.svg',dpi=300,format=\"svg\",bbox_inches = 'tight')"
    ]
   },
   {
@@ -1727,7 +1727,7 @@
     "    plt.xlabel('Episode')\n",
     "    plt.ylabel('Performance')\n",
     "\n",
-    "    plt.savefig(f'result_pictures/single_problem_in_continue/F{problem}.svg',dpi=300,format=\"svg\",bbox_inches = 'tight')"
+    "    plt.savefig(f'paper_pictures/single_problem_in_continue/F{problem}.svg',dpi=300,format=\"svg\",bbox_inches = 'tight')"
    ]
   }
  ],
diff --git a/draw/util.py b/draw/util.py
index d7f2e6f..1c3d920 100644
--- a/draw/util.py
+++ b/draw/util.py
@@ -1,106 +1,129 @@
-import re
-from collections import defaultdict
-import os
-from datetime import datetime
+"""Shared data-loading and plotting helpers for the paper notebooks."""
+
+from __future__ import annotations
+
+import copy
 import pickle
+import re
+from pathlib import Path
+
 import matplotlib.pyplot as plt
-import copy
+
 
 def read_data(filename):
-    pattern = re.compile(r'\d+\.\d+|\d+')
-    keyword1 = 'total env-steps'
-    keyword2 = 'return mean'
-    total_step =[]
-    mean_var =[]
-    with open(filename, 'r', encoding='utf-8') as file:
-        for line in file:
-            if keyword1 in line:
-                numbers = pattern.findall(line)
-                total_step.append(int(numbers[-1]))
-            if keyword2 in line:
-                numbers = pattern.findall(line)
-                numbers = [float(num) for num in numbers]
-                mean_var.append(numbers)
-
-    return_means = [d[-2] for d in mean_var]
-    return_stds = [d[-1] for d in mean_var]
-    return total_step,return_means,return_stds
+    pattern = re.compile(r"\d+\.\d+|\d+")
+    total_steps = []
+    mean_variance = []
+    with Path(filename).open("r", encoding="utf-8") as stream:
+        for line in stream:
+            if "total env-steps" in line:
+                total_steps.append(int(pattern.findall(line)[-1]))
+            if "return mean" in line:
+                mean_variance.append(
+                    [float(number) for number in pattern.findall(line)]
+                )
 
+    means = [values[-2] for values in mean_variance]
+    variances = [values[-1] for values in mean_variance]
+    return total_steps, means, variances
 
-def read_data_for_transformer(filename):
-    pattern =  re.compile(r'-?\d+\.\d+|-?\d+')
-    keyword = 'step :'
-    total_step =[]
-    data =[]
-    with open(filename, 'r', encoding='utf-8') as file:
-        for line in file:
-            if keyword in line:
-                line = line.split('step :')[1].split('Training')[0]
-                numbers = pattern.findall(line)
-                numbers = [float(num) for num in numbers]
-                data.append(numbers)
 
+def read_data_for_transformer(filename):
+    pattern = re.compile(r"-?\d+\.\d+|-?\d+")
+    data = []
+    with Path(filename).open("r", encoding="utf-8") as stream:
+        for line in stream:
+            if "step :" not in line:
+                continue
+            segment = line.split("step :", maxsplit=1)[1].split("Training", maxsplit=1)[
+                0
+            ]
+            data.append([float(number) for number in pattern.findall(segment)])
     return data
 
-def plot_each(plt,steps,means,stds,label,show_stds = False):
-    plt.plot(steps, means, label=label,linewidth=2)
+
+def plot_each(plotter, steps, means, deviations, label, show_stds=False):
+    plotter.plot(steps, means, label=label, linewidth=2)
     if show_stds:
-        plt.fill_between(steps,
-                         [m - s for m, s in zip(means, stds)],
-                         [m + s for m, s in zip(means, stds)],
-                          alpha=0.2)
-        
-def read_from_pkl(pkls):
-    datas = []
-    for pkl in pkls:
-        with open(pkl, 'rb') as f:
-            loaded_data = pickle.load(f)
-            datas.append(loaded_data)
-    print(len(datas))
-    return datas
-
-def roll_and_draw(df,problem_set,save_path):
-    
-    if not os.path.exists(save_path):
-        os.makedirs(save_path)
-
-    _new_df = copy.deepcopy(df)
-    new_df = _new_df.rolling(window=5, center=False).mean() 
-    show_std = True
+        plotter.fill_between(
+            steps,
+            [mean - deviation for mean, deviation in zip(means, deviations)],
+            [mean + deviation for mean, deviation in zip(means, deviations)],
+            alpha=0.2,
+        )
+
+
+def read_from_pkl(paths):
+    data = []
+    for path in paths:
+        with Path(path).open("rb") as stream:
+            data.append(pickle.load(stream))
+    return data
+
+
+def _prepare_output(save_path):
+    output = Path(save_path)
+    output.mkdir(parents=True, exist_ok=True)
+    return output
+
+
+def roll_and_draw(frame, problem_set, save_path):
+    output = _prepare_output(save_path)
+    smoothed = copy.deepcopy(frame).rolling(window=5, center=False).mean()
     for problem in problem_set:
         plt.figure(figsize=(5, 3))
-        plot_each(plt, new_df['Epoch'], new_df[f'Means_{problem}'], new_df[f'Vars_{problem}'], f'problem{problem}',show_stds=show_std)
-        plt.title(f'F{problem}')
-        plt.xlabel('Episode')
-        plt.ylabel('Performance')
-        if show_std:
-            plt.savefig(f'{save_path}\F{problem}.svg',dpi=300,format="svg",bbox_inches = 'tight')
-        else:
-            plt.savefig(f'{save_path}\F{problem}.svg',dpi=300,format="svg",bbox_inches = 'tight')
-
-def roll_and_draw_mutil(dfs,problem_set,display_name,save_path):
-    
-    if not os.path.exists(save_path):
-        os.makedirs(save_path)
-
-    new_dfs = []
-    for df in dfs:
-        _new_df = copy.deepcopy(df)
-        new_df = _new_df.rolling(window=5, center=False).mean() 
-        new_dfs.append(new_df)
-        
-    show_std = True
+        plot_each(
+            plt,
+            smoothed["Epoch"],
+            smoothed[f"Means_{problem}"],
+            smoothed[f"Vars_{problem}"],
+            f"problem{problem}",
+            show_stds=True,
+        )
+        plt.title(f"F{problem}")
+        plt.xlabel("Episode")
+        plt.ylabel("Performance")
+        plt.savefig(
+            output / f"F{problem}.svg",
+            dpi=300,
+            format="svg",
+            bbox_inches="tight",
+        )
+
+
+def roll_and_draw_multiple(frames, problem_set, display_names, save_path):
+    output = _prepare_output(save_path)
+    smoothed_frames = [
+        copy.deepcopy(frame).rolling(window=5, center=False).mean() for frame in frames
+    ]
     for problem in problem_set:
         plt.figure(figsize=(5, 3))
-        for new_df,_display_name in zip(new_dfs,display_name):
-            plot_each(plt, new_df['Epoch'], new_df[f'Means_{problem}'], new_df[f'Vars_{problem}'], _display_name, show_stds=show_std)
-        plt.title(f'F{problem}')
-        plt.xlabel('Episode')
-        plt.ylabel('Performance')
+        for frame, display_name in zip(smoothed_frames, display_names):
+            plot_each(
+                plt,
+                frame["Epoch"],
+                frame[f"Means_{problem}"],
+                frame[f"Vars_{problem}"],
+                display_name,
+                show_stds=True,
+            )
+        plt.title(f"F{problem}")
+        plt.xlabel("Episode")
+        plt.ylabel("Performance")
         plt.legend()
-        path = f'{save_path}F{problem}.svg'
-        print(path)
-        if show_std:
-            plt.savefig(f'{save_path}F{problem}.svg',dpi=300,format="svg",bbox_inches = 'tight')
-        else:
-            plt.savefig(f'{save_path}F{problem}.svg',dpi=300,format="svg",bbox_inches = 'tight')
\ No newline at end of file
+        plt.savefig(
+            output / f"F{problem}.svg",
+            dpi=300,
+            format="svg",
+            bbox_inches="tight",
+        )
+
+
+__all__ = [
+    "plot_each",
+    "read_data",
+    "read_data_for_transformer",
+    "read_from_pkl",
+    "roll_and_draw",
+    "roll_and_draw_multiple",
+]
diff --git a/matlab/AutoOpt.m b/matlab/AutoOpt.m
deleted file mode 100644
index 3b97fc0..0000000
--- a/matlab/AutoOpt.m
+++ /dev/null
@@ -1,146 +0,0 @@
-function AutoOpt(varargin)
-% --------------------------Introduction-----------------------------------
-% AutoOptLib is a MATLAB library for automatically designing metaheuristic
-% optimization algorithms. 
-
-% AutoOptLib is developed and actively maintained by the Swarm Intelligence
-% Lab at the Department of Computer Science and Engineering, Southern 
-% University of Science and Technology.
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% AutoOptLib is distributed in the hope that it will be useful, but WITHOUT
-% ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or 
-% FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License for
-% more details. You should have received a copy of the GNU General Public 
-% License along with this library. If not, see <https://www.gnu.org/licenses/>.
-
-% Please read the documentation at <https://github.com/qz89/AutoOpt> for a 
-% step-by-step user guidance.
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-
-% For any question, comment or suggestion, please contact Dr. Qi Zhao at 
-% <zhaoq@sustech.edu.cn>.
-% ----------------------------Settings-------------------------------------
-% Settings of the targeted problem:
-% Problem      : problem name
-% InstanceTrain: indexes of training instances
-% InstanceTest : indexes of test instances
-% 
-% Settings of the designed algorithm(s):
-% Setting.Mode    : design/solve, i.e., the aim is to designing algorithm or solving problem
-% Setting.AlgP    : number of search pathways in a designed algorithm
-% Setting.AlgQ    : maximum number of search operators in a search pathway
-% Setting.Archive : name of the archive(s) that will be used in the designed algorithm(s)
-% Setting.LSRange : range of parameter values that make the algorithm perform local search
-% Setting.IncRate : minimum rate of solutions' fitness increase during 3 consecutive iterations
-% Setting.InnerFE : maximum number of function evaluations for each call of local search
-% 
-% Settings of the design process:
-% Setting.AlgN    : number of algorithms to be designed
-% Setting.AlgRuns : number of algorithm runs on each problem instance
-% Setting.ProbN   : population size of the designed algorithms on the targeted problem instances
-% Setting.ProbFE  : number of fitness evaluations of the designed algorithms on the targeted problem instances
-% Setting.Metric  : quality/runtimeFE/runtimeSec/auc, i.e., metric for evaluating algorithms' performance 
-% Setting.Generate: search/learn, i.e., method for generating algorithms
-% Setting.Evaluate: exact/approximate/intensification/racing, i.e., method for evaluating algoritm's performance
-% Setting.Compare : average/statistic, i.e., method for comparing the performance of algorithms
-% Setting.AlgFE   : maximum number of algorithm evaluations during the design process (termination condition of the design process)
-% Setting.Tmax    : maximum running time measured by the number of function evaluations or wall clock time (in second)
-% Setting.Thres   : the lowest acceptable performance of the designed algorithms. The performance can be the solution quality
-% Setting.RacingK : number of instances evaluated before the first round of racing
-% Setting.Surro   : number of exact performance evaluations when using surrogate 
-% 
-% Settings of solving the targeted problem:
-% Setting.Alg     : algorithm file name, e.g., Algs
-% 
-% Example of running AutoOpt: 
-%    AutoOpt()
-%    AutoOpt('Mode','design','Problem','CEC2005_f1','InstanceTrain',[1,2],'InstanceTest',3)
-%    AutoOpt('Mode','design','Problem','CEC2005_f1','InstanceTrain',[1,3],'InstanceTest',2,'AlgN',2,'AlgFE',4,'AlgRuns',1,'Compare','average')
-%    AutoOpt('Mode','solve','Problem','CEC2005_f1','InstanceSolve',[1,2],'AlgFile','Algs','ProbN',10,'ProbFE',100,'AlgRuns',2)
-% -------------------------------------------------------------------------
-
-if nargin == 0 % call the GUI
-    APP;
-else 
-    % get mode
-    if any(strcmp(varargin,'Mode'))
-        Setting = struct;
-        ind = find(strcmp(varargin,'Mode'));
-        Setting.Mode = varargin{ind+1};
-    else
-        error('Please set the mode to "design" or "solve".');
-    end
-
-    % get problem
-    if strcmp(Setting.Mode,'design')
-        [prob,instanceTrain,instanceTest] = Input(varargin,Setting,'data');
-
-    elseif strcmp(Setting.Mode,'solve')
-        [prob,instanceSolve] = Input(varargin,Setting,'data');
-    end
-    
-    %get problem only use run pbo problem
-    if any(strcmp(varargin,'problem_id'))
-        ind = find(strcmp(varargin,'problem_id'));
-        Setting.problem_id = varargin{ind+1};
-    end
-
-    % default parameters
-    switch Setting.Mode
-        case 'design'
-            Setting.AlgP     = 1;
-            Setting.AlgQ     = 3;
-            Setting.Archive  = '';
-            Setting.IncRate  = 0.05;
-            Setting.ProbN    = 50;
-            Setting.ProbFE   = 5000;
-            Setting.InnerFE  = 100;
-            Setting.AlgN     = 100;
-            Setting.AlgFE    = 3200;
-            Setting.AlgRuns  = 5;
-            Setting.Metric   = 'quality'; % quality/runtimeFE/runtimeSec/auc
-            Setting.Generate = 'search';   % search/learn
-            Setting.Evaluate = 'exact';   % exact/approximate/intensification/racing
-            Setting.Compare  = 'average'; % average/statistic
-            Setting.Tmax     = [];
-            Setting.Thres    = [];
-            Setting.LSRange  = 0.3;
-            Setting.RacingK  = max(1,round(length(instanceTrain)*0.2)); 
-            Setting.Surro    = Setting.ProbFE*0.3;
-            Setting          = Input(varargin,Setting,'parameter'); % replace default parameters with user-defined ones
-            Setting          = Input(Setting,'check'); % avoid conflicting parameter settings
-            [algs,algTrace]  = Process(prob,instanceTrain,instanceTest,Setting);
-            Output(algs,algTrace,instanceTrain,instanceTest,Setting);
-
-        case 'solve'
-            Setting.Mode = 'solve';
-            Setting.AlgFile  = '';
-            Setting.AlgName  = 'Continuous Genetic Algorithm';
-            Setting.Metric   = 'quality';
-            Setting.Tmax     = [];
-            Setting.Thres    = [];
-            Setting.ProbN    = 100;
-            Setting.ProbFE   = 50000;
-            Setting.AlgRuns  = 31;
-            Setting          = Input(varargin,Setting,'parameter');
-            Setting          = Input(Setting,'check');
-            [bestSolutions,allSolutions] = Process(prob,instanceSolve,Setting);
-            Output(bestSolutions,allSolutions,instanceSolve,Setting);
-    end
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/archive_best.m b/matlab/Components/archive_best.m
deleted file mode 100644
index a2e9d9a..0000000
--- a/matlab/Components/archive_best.m
+++ /dev/null
@@ -1,46 +0,0 @@
-function [output1,output2] = archive_best(varargin)
-% Collect the best (in terms of fitness) solution of each iteration.
-
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Solution    = varargin{1};
-        CurrArchive = varargin{2};
-
-        Fitness  = Solution.fits;
-        [~,best] = min(Fitness);
-        output1  = [CurrArchive,Solution(best)];
-    
-    case 'parameter'
-        % no parameter
-
-    case 'behavior'
-        output1 = {'';''}; 
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/archive_diversity.m b/matlab/Components/archive_diversity.m
deleted file mode 100644
index ecdc689..0000000
--- a/matlab/Components/archive_diversity.m
+++ /dev/null
@@ -1,66 +0,0 @@
-function [output1,output2] = archive_diversity(varargin)
-% Collect the N most diversified solutions found so far to an external
-% archive.
-
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Solution    = varargin{1};
-        CurrArchive = varargin{2};
-        Problem     = varargin{3};
-
-        Solution = [Solution,CurrArchive];
-
-        % distance between each pair of solutions
-        if contains(Problem.type{1},'continuous')
-            dist = pdist2(Solution.decs,Solution.decs,'euclidean'); % n*n, n is the number of solutions
-        else
-            dist = pdist2(Solution.decs,Solution.decs,'hamming');
-        end
-
-        ind = [randi(length(Solution));zeros(Problem.N-1,1)];
-        for i = 2:Problem.N
-            % sort the total distance between each solution and the archive solutions in descending order
-            [~,rank] = sort(sum(dist(ind(1:i-1),:),1),'descend');
-            j = 1;
-            ind(i) = rank(j); % select the solution with the largest distance to the archive solutions
-            while ismember(ind(i),ind(1:i-1))
-                j = j+1;
-                ind(i) = rank(j);
-            end
-        end
-        output1 = Solution(ind);
-        
-    case 'parameter'
-        % no parameter
-
-    case 'behavior'
-        output1 = {'';''}; 
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/archive_statistic.m b/matlab/Components/archive_statistic.m
deleted file mode 100644
index a6c5a9a..0000000
--- a/matlab/Components/archive_statistic.m
+++ /dev/null
@@ -1,47 +0,0 @@
-function [output1,output2] = archive_statistic(varargin)
-% Collect the average and standard deviation of solution's fitness of each
-% iteration.
-
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Solution    = varargin{1};
-        CurrArchive = varargin{2};
-
-        Fitness = Solution.fits;
-        output1 = [mean(Fitness),std(Fitness)];
-        output1 = [CurrArchive;output1];
-    
-    case 'parameter'
-        % no parameter
-
-    case 'behavior'
-        output1 = {'';''}; 
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/archive_tabu.m b/matlab/Components/archive_tabu.m
deleted file mode 100644
index 574e246..0000000
--- a/matlab/Components/archive_tabu.m
+++ /dev/null
@@ -1,45 +0,0 @@
-function [output1,output2] = archive_tabu(varargin)
-% Collect N solutions to the tabu list.
-
-%------------------------------Reference-----------------------------------
-% Glover F. Tabu search—part I[J]. ORSA Journal on computing, 1989, 1(3): 
-% 190-206.
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Solution = varargin{1};
-        output1  = Solution; % prevent the search visiting the last N solutions.
-
-    case 'parameter'
-        % no parameter
-
-    case 'behavior'
-        output1 = {'';''};
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/choose_brainstorm.m b/matlab/Components/choose_brainstorm.m
deleted file mode 100644
index b185559..0000000
--- a/matlab/Components/choose_brainstorm.m
+++ /dev/null
@@ -1,86 +0,0 @@
-function [output1,output2] = choose_brainstorm(varargin)
-% Brain storm optimization's idea picking up for selecting solutions.
-
-%------------------------------Reference-----------------------------------
-% Shi Y. An optimization algorithm based on brainstorming process[M]//
-% Emerging Research on Swarm Intelligence and Algorithm Optimization. IGI 
-% Global, 2015: 1-35.
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Solution = varargin{1};
-        Problem  = varargin{2};
-        Para     = varargin{3};
-
-        K     = max(2,round(Para(1))); % number of clusters
-        gamma = Para(2); % probability of selecting cluster centers
-
-        % clustering
-        if contains(Problem(1).type{1},'continuous')
-            ClusterInd = kmeans(Solution.decs,K,'Distance','sqeuclidean'); % clustering in continuous search space
-        else
-            error('choose_cluster is only available for continuous problems.'); 
-        end
-
-        Cluster = cell(K,1);
-        ClusterCenter = zeros(K,1);
-        SelRateCenter = zeros(K,1);
-        Objs = Solution.objs;
-        for i = 1:K
-            Cluster{i} = find(ClusterInd == i); % indexes of solutions belonging to cluster i
-            [~,ind] = min(Objs(Cluster{i},:)); % fittest solution
-            ClusterCenter(i) = Cluster{i}(ind); % record the fittest solution as cluster center
-            SelRateCenter(i) = length(Cluster{i})/length(Solution); % select centers according to their scales
-            Cluster{i}(ind) = []; % reserve solutions except for the center
-        end
-        temp = zeros(K,1);
-        for i = 1:K
-            temp(i) = SelRateCenter(i)./sum(SelRateCenter);
-        end
-        SelRateCenter = temp;
-
-        % choose
-        index = randsrc(Problem(1).N,1,[ClusterCenter';SelRateCenter']); % select N cluster centers from 1:K, some of them may be the same
-        for i = 1:Problem(1).N % select cluster center with probability gamma, then replace the center with a random solution from the cluster
-            if rand <= gamma && ~isempty(Cluster{ClusterCenter==index(i)})
-                ind = randi(numel(Cluster{ClusterCenter==index(i)}));
-                index(i) = Cluster{ClusterCenter==index(i)}(ind);
-            end
-        end
-        output1 = index;
-
-    case 'parameter'
-        Problem = varargin{1};
-        k_max   = max(1,round(Problem(1).N/5)); % maximum number of clusters in brain storm optimization's idea picking up
-        output1 = [1,k_max;0,1]; % number of clusters and probability of selecting cluster center in BSO's exploration
-
-    case 'behavior'
-        output1 = {'';''}; 
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/choose_nich.m b/matlab/Components/choose_nich.m
deleted file mode 100644
index 4b3bf67..0000000
--- a/matlab/Components/choose_nich.m
+++ /dev/null
@@ -1,158 +0,0 @@
-function [output1,output2] = choose_nich(varargin)
-% Adaptive niching based on the nearest-better clustering. Crossover 
-% (if available) will be conducted between solutions from the same specie.
-
-%------------------------------Reference-----------------------------------
-% Preuss M. Niching the CMA-ES via nearest-better clustering[C]//
-% Proceedings of the 12th annual conference companion on Genetic and 
-% evolutionary computation. 2010: 1711-1718.
-
-% Yan B, Zhao Q, Li M, et al. Fitness landscape analysis and niching 
-% genetic approach for hybrid beamforming in RIS-aided communications[J].
-% Applied Soft Computing, 2022, 131: 109725.
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Solution = varargin{1};
-        Problem  = varargin{2};
-        G        = varargin{5};
-        
-        disance = pdist2(Solution.decs,Solution.decs); % compute distance
-        species = mNBC(disance,1,-1,G,Problem.Gmax); % divide species
-        
-        index = [];
-        for i = 1:length(species)
-            currInd = species(i).idx;
-            currInd = currInd(randperm(numel(currInd)));
-            index = [index;currInd]; % gather all solutions' indexes 
-        end
-        
-        if mod(length(index),2) == 1 % if the number of solutions is odd 
-            odd = true;
-            index = [index;index(end)];
-        else
-            odd = false;
-        end
-
-        % crossover between solutions from the same specie
-        index = reshape(index,2,length(index)/2);
-        index = index';
-        index = [index(:,1);index(:,2)];
-        if odd == true
-            index(end) = [];
-        end
-
-        output1 = index;
-
-    case 'parameter'
-        % n/a
-
-    case 'behavior'
-        output1 = {'';''}; 
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
-
-function species = mNBC(disance,fai,min_size,g,gmax)
-% matdis: the distance between each pair of the individuals
-% fai: the weight in NBC
-% min_size: the min size of the species
-% D: the dimension ofthe problem
-% g: the current generation
-
-    % reset 'min_size' if 'min_size' is equal to -1
-    if min_size == -1
-        min_size = round(3+g/gmax*7);
-    end 
-    
-    % find the nearest better neighour and the distance of each individual
-    NP = size(disance, 1);
-    nbc = zeros(NP, 3);
-    nbc(1, :) = [1 -1 0]; % the best individual do not have the nearest better neighbour
-    for i = 2:NP
-        nbc(i, 1) = i;
-        [nbc(i, 3), nbc(i, 2)] = min(disance(i, 1:i-1));
-    end
-    
-    % set the follow value
-    follow = ones(NP, 1);
-    for i = NP:-1:2 
-        follow(nbc(i, 2)) = follow(nbc(i, 2)) + follow(i); % the subtree rooted at nearest better neighbour individual must contain all current individual's nodes
-    end
-    
-    % cut the edge from the longest to the shortest
-    meandis = fai * mean(nbc(2:NP, 3));
-    seeds = 1;
-    
-    [~, sort_index] = sort(nbc(:, 3), 'descend');
-    for i = 1:NP
-        if nbc(sort_index(i), 3) > meandis % one of the cut conditions
-            inf_index = sort_index(i); % the inferior individual
-            sup_index = nbc(sort_index(i), 2); % the superior individual
-            top_index = sup_index; % the root of the subtree which contains the inferior and superior individuals
-            while nbc(top_index, 2) ~= -1
-                top_index = nbc(top_index, 2);
-                sup_index = [sup_index, top_index];
-            end
-            
-            if follow(inf_index) >= min_size && follow(top_index) - follow(inf_index) >= min_size % the other of the cut conditions
-                % cut operator
-                nbc(inf_index, 2) = -1;
-                nbc(inf_index, 3) = 0;
-                % put the current seed into the set
-                seeds = [seeds; sort_index(i)];
-                follow(sup_index) = follow(sup_index) - follow(inf_index);
-            end
-        end
-    end
-    
-    % set the root of subtree which contain the current individual
-    m = zeros(NP, 2);
-    m(1:NP, 1) = 1:NP;
-    for i = 1:NP
-       j = nbc(i, 2);
-       k = j;
-       while j ~= -1
-           k =j;
-           j = nbc(j, 2);
-       end
-       if k == -1
-           m(i, 2) = i;
-       else
-           m(i, 2) = k;
-       end
-    end
-    
-    % construct the result
-    species = struct();
-    for i=1:length(seeds)
-       species(i).seed = seeds(i);
-       species(i).idx = m(m(:, 2) == seeds(i), 1);
-       species(i).len = length(species(i).idx);
-    end  
-end
\ No newline at end of file
diff --git a/matlab/Components/choose_roulette_wheel.m b/matlab/Components/choose_roulette_wheel.m
deleted file mode 100644
index 3f45193..0000000
--- a/matlab/Components/choose_roulette_wheel.m
+++ /dev/null
@@ -1,56 +0,0 @@
-function [output1,output2] = choose_roulette_wheel(varargin)
-% Roulette wheel selection.
-
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Solution = varargin{1};
-        Problem  = varargin{2};
-        Fitness = Solution.fits;
-
-        % according to fitness rank
-        % S = 2;
-        % [~,rank] = sort(Fitness); % for single-objective only
-        % Prob = (2-S)/N + 2.*rank.*(S-1)./(N*(N-1));
-
-        % according to fitness proportation
-        Fitness = reshape(Fitness,1,[]);
-        Fitness = Fitness-min(min(Fitness),0)+1e-6;
-        Fitness = cumsum(1./Fitness);
-        prob    = Fitness./max(Fitness);
-        index   = arrayfun(@(S)find(rand<=prob,1),1:Problem.N);
-        output1 = index;
-    
-    case 'parameter'
-        % no parameter
-
-    case 'behavior'
-        output1 = {'';''};
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/choose_tournament.m b/matlab/Components/choose_tournament.m
deleted file mode 100644
index 98d6677..0000000
--- a/matlab/Components/choose_tournament.m
+++ /dev/null
@@ -1,51 +0,0 @@
-function [output1,output2] = choose_tournament(varargin)
-% K-tournament selection.
-
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Solution = varargin{1};
-        Problem  = varargin{2};
-        Fitness = Solution.fits;
-        K = 2;
-
-        MatchIndex = randi(size(Fitness,1),Problem.N,K);
-        Fitness = repmat(Fitness,1,K);
-        MatchFitness = Fitness(MatchIndex);
-        index = MatchIndex(MatchFitness == min(MatchFitness,[],2));
-        index = index(1:Problem.N);
-        output1 = index;
-
-    case 'parameter'
-        % no parameter
-
-    case 'behavior'
-        output1 = {'';''}; 
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/choose_traverse.m b/matlab/Components/choose_traverse.m
deleted file mode 100644
index d7c27ed..0000000
--- a/matlab/Components/choose_traverse.m
+++ /dev/null
@@ -1,43 +0,0 @@
-function [output1,output2] = choose_traverse(varargin)
-% Select each of the cureent soutions.
-
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Problem  = varargin{2};
-        index = 1:Problem.N;
-        output1 = index;
-
-    case 'parameter'
-        % no parameter
-
-    case 'behavior'
-        output1 = {'';''}; 
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/cross_arithmetic.m b/matlab/Components/cross_arithmetic.m
deleted file mode 100644
index 9d51e8f..0000000
--- a/matlab/Components/cross_arithmetic.m
+++ /dev/null
@@ -1,55 +0,0 @@
-function [output1,output2] = cross_arithmetic(varargin)
-% Whole arithmetic crossover.
-
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Parent  = varargin{1};
-        Para    = varargin{3};
-        Aux    = varargin{4};
-
-        Prob    = Para;
-        Parent  = Parent.decs;
-        N       = size(Parent,1);
-        Parent1 = Parent(1:ceil(N/2),:);
-        Parent2 = Parent(floor(N/2)+1:end,:);
-
-        Offspring1 = Prob.*Parent1+(1-Prob).*Parent2;
-        Offspring2 = Prob.*Parent2+(1-Prob).*Parent1;
-        Offspring  = [Offspring1;Offspring2];       
-        output1    = Offspring(1:N,:);
-        output2    = Aux;
-
-    case 'parameter'
-        output1 = [0,0.3]; % range of crossover probability
-
-    case 'behavior'
-        output1 = {'';'GS'}; % always perform global search
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/cross_order_n.m b/matlab/Components/cross_order_n.m
deleted file mode 100644
index b544764..0000000
--- a/matlab/Components/cross_order_n.m
+++ /dev/null
@@ -1,80 +0,0 @@
-function [output1,output2] = cross_order_n(varargin)
-% N-order crossover.
-
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Parent = varargin{1};
-        Para   = varargin{3};
-        Aux    = varargin{4};
-
-        n = round(Para);
-        Parent = Parent.decs;
-        [N,D] = size(Parent);
-        Parent1 = Parent(1:ceil(N/2),:);
-        Parent2 = Parent(floor(N/2)+1:end,:);
-        Nhalf = size(Parent1,1);
-
-        Offspring1 = Parent1;
-        Offspring2 = Parent2;
-
-        seed = zeros(Nhalf,n);
-        for i = 1:Nhalf
-            seed(i,:) = randperm(D,n); % the n points to be exchanged
-        end
-
-        for i = 1:Nhalf
-            k = seed(i,:);
-            Temp = setdiff(Parent2(i,:),Parent1(i,k),'stable'); % variables of parent 2 that are not appeared in the selected segement (k) of parent 1
-            ind  = setdiff(1:D,k,'stable'); % indexes of variables of parent 2 that are not appeared in the selected segement (k) of parent 1
-            Offspring1(i,ind) = Temp;
-
-            Temp = setdiff(Parent1(i,:),Parent2(i,k),'stable'); % variables of parent 1 that are not appeared in the selected segement (k) of parent 2
-            ind  = setdiff(1:D,k,'stable'); % indexes of variables of parent 1 that are not appeared in the selected segement (k) of parent 2
-            Offspring2(i,ind) = Temp;
-        end
-        Offspring = [Offspring1;Offspring2];
-        output1   = Offspring(1:N,:);
-        output2   = Aux;
-
-    case 'parameter'
-        % number of problem's decision variable
-        Problem  = varargin{1};
-        D = zeros(length(Problem),1);
-        for i = 1:length(Problem)
-            D(i) = size(Problem(i).bound,2);
-        end
-        D = min(D);
-        n_max = max(1,round(D*0.5)); % the maximum n in n-point crossover
-        output1 = [1,n_max]; % the n point
-
-    case 'behavior'
-        output1 = {'LS','small';'GS','large'}; % small n values perform local search
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/cross_order_two.m b/matlab/Components/cross_order_two.m
deleted file mode 100644
index 37b8e6c..0000000
--- a/matlab/Components/cross_order_two.m
+++ /dev/null
@@ -1,67 +0,0 @@
-function [output1,output2] = cross_order_two(varargin)
-% Two-order crossover.
-
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Parent = varargin{1};
-        Aux    = varargin{4};
-        Parent = Parent.decs;
-        [N,D] = size(Parent);
-        Parent1 = Parent(1:ceil(N/2),:);
-        Parent2 = Parent(floor(N/2)+1:end,:);
-        Nhalf = size(Parent1,1);
-
-        Offspring1 = Parent1;
-        Offspring2 = Parent2;
-        for i = 1:Nhalf
-            k = randperm(D,2);
-            k = sort(k,'ascend');
-            Temp = setdiff(Parent2(i,:),Parent1(i,k(1):k(2)),'stable'); % variables of parent 2 that are not appeared in the selected segement (k(1):k(2)) of parent 1
-            ind  = setdiff(1:D,k(1):k(2),'stable'); % indexes of variables of parent 2 that are not appeared in the selected segement (k(1):k(2)) of parent 1
-            Offspring1(i,ind) = Temp;
-
-
-            k = randperm(D,2);
-            k = sort(k,'ascend');
-            Temp = setdiff(Parent1(i,:),Parent2(i,k(1):k(2)),'stable'); % variables of parent 1 that are not appeared in the selected segement (k(1):k(2)) of parent 2
-            ind  = setdiff(1:D,k(1):k(2),'stable'); % indexes of variables of parent 1 that are not appeared in the selected segement (k(1):k(2)) of parent 2
-            Offspring2(i,ind) = Temp;
-        end
-        Offspring = [Offspring1;Offspring2];
-        output1   = Offspring(1:N,:);
-        output2   = Aux;
-
-    case 'parameter'
-        % no parameter
-
-    case 'behavior'
-        output1 = {'';'GS'};  % always perform global search
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/cross_point_n.m b/matlab/Components/cross_point_n.m
deleted file mode 100644
index ec69120..0000000
--- a/matlab/Components/cross_point_n.m
+++ /dev/null
@@ -1,72 +0,0 @@
-function [output1,output2] = cross_point_n(varargin)
-% Randomly select n indices and exchange the n elements in these n indices
-% between a pair of solutions. Similar with the cross_uniform operator.
-
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Parent = varargin{1};
-        Para   = varargin{3};
-        Aux    = varargin{4};
-        
-        Parent = Parent.decs;
-        n = max(round(Para),1);
-        [N,D] = size(Parent);
-        Parent1 = Parent(1:ceil(N/2),:);
-        Parent2 = Parent(floor(N/2)+1:end,:);
-        Nhalf = size(Parent1,1);
-
-        k = zeros(Nhalf,n);
-        for i = 1:Nhalf
-            k(i,:) = randperm(D,n); % the n points to be exchanged
-        end
-
-        Offspring1 = Parent1;
-        Offspring2 = Parent2;
-        Offspring1(:,k(:,n)) = Parent2(:,k(:,n));
-        Offspring2(:,k(:,n)) = Parent1(:,k(:,n));
-        Offspring = [Offspring1;Offspring2];
-        output1   = Offspring(1:N,:);
-        output2   = Aux;
-
-    case 'parameter'
-        % number of problem's decision variable
-        Problem  = varargin{1};
-        D = zeros(length(Problem),1);
-        for i = 1:length(Problem)
-            D(i) = size(Problem(i).bound,2);
-        end
-        D = min(D);
-        n_max = D; % the maximum n in n-point crossover
-        output1 = [1,n_max]; % the n point
-
-    case 'behavior'
-        output1 = {'LS','small';'GS','large'}; % small n values perform local search
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/cross_point_one.m b/matlab/Components/cross_point_one.m
deleted file mode 100644
index 98befbc..0000000
--- a/matlab/Components/cross_point_one.m
+++ /dev/null
@@ -1,57 +0,0 @@
-function [output1,output2] = cross_point_one(varargin)
-% One-point crossover.
-
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Parent = varargin{1};
-        Aux    = varargin{4};
-        
-        Parent = Parent.decs;
-        [N,D] = size(Parent);
-        Parent1 = Parent(1:ceil(N/2),:);
-        Parent2 = Parent(floor(N/2)+1:end,:);
-        Nhalf = size(Parent1,1);
-
-        k = randi(D,Nhalf,1);
-        Offspring1 = Parent1;
-        Offspring2 = Parent2;
-        Offspring1(:,k:end) = Parent2(:,k:end);
-        Offspring2(:,k:end) = Parent1(:,k:end);
-        Offspring = [Offspring1;Offspring2];
-        output1   = Offspring(1:N,:);
-        output2   = Aux;
-
-    case 'parameter'
-        % n/a
-
-    case 'behavior'
-        output1 = {'';'GS'}; % always perform global search
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/cross_point_two.m b/matlab/Components/cross_point_two.m
deleted file mode 100644
index c019ecc..0000000
--- a/matlab/Components/cross_point_two.m
+++ /dev/null
@@ -1,68 +0,0 @@
-function [output1,output2] = cross_point_two(varargin)
-% The two point crossover operation in genetic algorithm.
-
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Parent = varargin{1};
-        Aux    = varargin{4};
-
-        Parent  = Parent.decs;
-        [N,D]   = size(Parent);
-        Parent1 = Parent(1:ceil(N/2),:);
-        Parent2 = Parent(floor(N/2)+1:end,:);
-        Nhalf   = size(Parent1,1);
-
-        if D < 2
-            error(['Two-point crossover is unavailable for 1-dimensional ' ...
-                'problems. Please remove the two-point crossover from the ' ...
-                'Design_Space.'])
-        end
-        
-        k = zeros(Nhalf,2);
-        for i = 1:Nhalf
-            k(i,:) = randperm(D,2);
-        end
-        k = sort(k,2);
-
-        Offspring1 = Parent1;
-        Offspring2 = Parent2;
-        Offspring1(:,k(:,1):k(:,2)) = Parent2(:,k(:,1):k(:,2));
-        Offspring2(:,k(:,1):k(:,2)) = Parent1(:,k(:,1):k(:,2));
-        Offspring = [Offspring1;Offspring2];
-        output1   = Offspring(1:N,:);
-        output2   = Aux;
-
-    case 'parameter'
-        % n/a
-
-    case 'behavior'
-        output1 = {'';'GS'}; % always perform global search
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/cross_point_uniform.m b/matlab/Components/cross_point_uniform.m
deleted file mode 100644
index e725fac..0000000
--- a/matlab/Components/cross_point_uniform.m
+++ /dev/null
@@ -1,60 +0,0 @@
-function [output1,output2] = cross_point_uniform(varargin)
-% Uniform crossover.
-
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Qi Zhao>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Parent = varargin{1};
-        Para   = varargin{3};
-        Aux    = varargin{4};
-
-        Prob = Para;
-        Parent = Parent.decs;
-        [N,D] = size(Parent);
-        Parent1 = Parent(1:ceil(N/2),:);
-        Parent2 = Parent(floor(N/2)+1:end,:);
-        Nhalf = size(Parent1,1);
-
-        ind = rand(Nhalf,D) < Prob;
-        Offspring1 = Parent1;
-        Offspring2 = Parent2;
-        Offspring1(ind) = Parent2(ind);
-        Offspring2(ind) = Parent1(ind);
-        Offspring = [Offspring1;Offspring2];
-        output1   = Offspring(1:N,:);
-        output2   = Aux;
-
-    case 'parameter'
-        output1 = [0.05,0.5]; % crossover probability
-
-    case 'behavior'
-        output1 = {'LS','small';'GS','large'}; % small probabilities perform local search
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/cross_sim_binary.m b/matlab/Components/cross_sim_binary.m
deleted file mode 100644
index 91c2ec0..0000000
--- a/matlab/Components/cross_sim_binary.m
+++ /dev/null
@@ -1,64 +0,0 @@
-function [output1,output2] = cross_sim_binary(varargin)
-% Simulated binary crossover.
-
-%------------------------------Reference-----------------------------------
-% Deb K, Agrawal R B. Simulated binary crossover for continuous search 
-% space[J]. Complex systems, 1995, 9(2): 115-148.
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Parent = varargin{1};
-        Para   = varargin{3};
-        Aux    = varargin{4};
-
-        Parent = Parent.decs;
-        DisC = Para;
-            
-        [N,D] = size(Parent);
-        Parent1 = Parent(1:ceil(N/2),:);
-        Parent2 = Parent(floor(N/2)+1:end,:);
-        Nhalf = size(Parent1,1);
-
-        beta = zeros(Nhalf,D);
-        mu = rand(Nhalf,D);
-
-        beta(mu<=0.5) = (mu(mu<=0.5)*2).^(1/(1+DisC));
-        beta(mu>0.5)  = (2-mu(mu>0.5)*2).^(-1/(1+DisC));
-        Offspring = [0.5*((1+beta).*Parent1+(1-beta).*Parent2);
-            0.5*((1-beta).*Parent1+(1+beta).*Parent2)];        
-        output1   = Offspring(1:N,:);
-        output2   = Aux;
-
-    case 'parameter'
-        output1 = [20,40]; % crossover distribution
-
-    case 'behavior'
-        output1 = {'';'GS'}; % always perform global search
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/para_cma.m b/matlab/Components/para_cma.m
deleted file mode 100644
index d2b132e..0000000
--- a/matlab/Components/para_cma.m
+++ /dev/null
@@ -1,80 +0,0 @@
-function Aux = para_cma(varargin)
-% Update CMA-ES's parameters.
-
-%------------------------------Reference-----------------------------------
-% N. Hansen and A. Ostermeier, Completely derandomized selfadaptation in
-% evolution strategies, Evolutionary Computation, 2001, 9(2): 159-195.
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-Solution = varargin{1};
-Problem  = varargin{2};
-Aux      = varargin{3};
-type     = varargin{4};
-
-% prepare parameters
-Disturb = Aux.cma_Disturb;
-halfN   = Aux.cma_halfN;
-w       = Aux.cma_w;
-betterN = Aux.cma_betterN;
-mean    = Aux.cma_mean;
-sigma   = Aux.cma_sigma;
-csigma  = Aux.cma_csigma;
-dsigma  = Aux.cma_dsigma;
-chiN     = Aux.cma_chiN;
-cc      = Aux.cma_cc;
-ccov    = Aux.cma_ccov;
-cmu     = Aux.cma_cmu;
-hth     = Aux.cma_hth;
-ps      = Aux.cma_ps;
-pc      = Aux.cma_pc;
-C       = Aux.cma_C;
-
-% update parameters
-switch type
-    case 'solution'
-        Fitness  = Solution.fits;
-    case 'algorithm'
-        Fitness  = Solution.avePerformAll;
-end
-[~,rank] = sort(Fitness,'ascend');
-Disturb  = Disturb(rank,:);
-DisturbW = w*Disturb(1:halfN,:);
-mean     = mean+sigma.*DisturbW;
-
-ps    = (1-csigma)*ps+sqrt(csigma*(2-csigma)*betterN)*DisturbW/chol(C)';
-hs    = norm(ps)/sqrt(1-(1-csigma)^(2*(Problem(1).Gmax+1))) < hth;
-pc    = (1-cc)*pc+hs*sqrt(cc*(2-cc)*betterN)*DisturbW;
-delta = (1-hs)*cc*(2-cc);
-C     = (1-ccov-cmu)*C+ccov*(pc'*pc+delta*C);
-for i = 1:halfN
-    C = C+cmu*w(i)*Disturb(i,:)'*Disturb(i,:);
-end
-[V,E] = eig(C);
-if any(diag(E)<0)
-    C = V*max(E,0)/V;
-end
-sigma = sigma*exp(csigma/dsigma*(norm(ps)/chiN-1))^0.3;
-
-Aux.cma_mean  = mean;
-Aux.cma_ps    = ps;
-Aux.cma_pc    = pc;
-Aux.cma_C     = C;
-Aux.cma_sigma = sigma;
-end
\ No newline at end of file
diff --git a/matlab/Components/para_pso.m b/matlab/Components/para_pso.m
deleted file mode 100644
index 09eff18..0000000
--- a/matlab/Components/para_pso.m
+++ /dev/null
@@ -1,34 +0,0 @@
-function Aux = para_pso(varargin)
-% Update pbest and gbest for PSO' particle fly operator.
-
-%------------------------------Reference-----------------------------------
-% Shi Y, Eberhart R. A modified particle swarm optimizer[C]//1998 IEEE 
-% International Conference on Evolutionary Computation Proceedings. IEEE 
-% World Congress on Computational Intelligence (Cat. No. 98TH8360). IEEE, 
-% 1998: 69-73.
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-Solution = varargin{1};
-Problem  = varargin{2};
-Aux      = varargin{3};
-Aux.Pbest = update_pairwise([Aux.Pbest,Solution],Problem,'execute');
-[~,best]  = min(Aux.Pbest.objs);
-Aux.Gbest = Aux.Pbest(best);
-end
\ No newline at end of file
diff --git a/matlab/Components/reinit_continuous.m b/matlab/Components/reinit_continuous.m
deleted file mode 100644
index 5390f23..0000000
--- a/matlab/Components/reinit_continuous.m
+++ /dev/null
@@ -1,49 +0,0 @@
-function [output1,output2] = reinit_continuous(varargin)
-% Reinitialization for continuous problems.
-
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Solution = varargin{1};
-        Problem  = varargin{2};
-        Aux      = varargin{4};
-
-        N = length(Solution);
-        Lower = Problem.bound(1,:);
-        Upper = Problem.bound(2,:);
-        output1 = unifrnd(repmat(Lower,N,1),repmat(Upper,N,1));
-        output2 = Aux;
-
-    case 'parameter'
-        % n/a
-
-    case 'behavior'
-        output1 = {'';'GS'}; % always perform global search
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/reinit_discrete.m b/matlab/Components/reinit_discrete.m
deleted file mode 100644
index 4bff7e8..0000000
--- a/matlab/Components/reinit_discrete.m
+++ /dev/null
@@ -1,50 +0,0 @@
-function [output1,output2] = reinit_discrete(varargin)
-% Reinitialization for discrete problems.
-
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Solution = varargin{1};
-        Problem  = varargin{2};
-        Aux      = varargin{4};
-
-        [N,D] = size(Solution.decs);
-        output1 = zeros(N,D);
-        for j = 1:D
-            output1(:,j) = randi([Problem.bound(1,j),Problem.bound(2,j)],N,1);
-        end
-        output2 = Aux;
-
-    case 'parameter'
-        % n/a
-
-    case 'behavior'
-        output1 = {'';'GS'}; % always perform global search
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/reinit_permutation.m b/matlab/Components/reinit_permutation.m
deleted file mode 100644
index 46fd689..0000000
--- a/matlab/Components/reinit_permutation.m
+++ /dev/null
@@ -1,46 +0,0 @@
-function [output1,output2] = reinit_permutation(varargin)
-% Reinitialization for permutation problems.
-
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Solution = varargin{1};
-        Aux      = varargin{4};
-
-        [N,D] = size(Solution.decs);
-        [~,output1] = sort(rand(N,D),2);
-        output2 = Aux;
-    
-    case 'parameter'
-        % n/a
-
-    case 'behavior'
-        output1 = {'';'GS'}; % always perform global search
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/search_cma.m b/matlab/Components/search_cma.m
deleted file mode 100644
index 15d46c3..0000000
--- a/matlab/Components/search_cma.m
+++ /dev/null
@@ -1,134 +0,0 @@
-function [output1,output2] = search_cma(varargin)
-% The evolution strategy with convariance matrix adaption.
-
-%------------------------------Reference-----------------------------------
-% N. Hansen and A. Ostermeier, Completely derandomized selfadaptation in
-% evolution strategies, Evolutionary Computation, 2001, 9(2): 159-195.
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Parent  = varargin{1};
-        Problem = varargin{2};
-        lower   = Problem.bound(1,:);
-        upper   = Problem.bound(2,:);
-        Aux     = varargin{4};
-        if ~isnumeric(Parent)
-            Parent  = Parent.decs;
-        end
-        [N,D] = size(Parent);
-
-        % initialize parameters
-        if ~isfield(Aux,'cma_halfN')
-            Aux.cma_halfN   = round(N/2);
-            w               = log(Aux.cma_halfN+0.5)-log(1:Aux.cma_halfN);
-            Aux.cma_w       = w./sum(w);
-            Aux.cma_betterN = 1/sum(Aux.cma_w.^2);
-
-            Aux.cma_csigma  = (Aux.cma_betterN+2)/(D+Aux.cma_betterN+5);
-            Aux.cma_dsigma  = Aux.cma_csigma+2*max(sqrt((Aux.cma_betterN-1)/(D+1))-1,0)+1;
-            Aux.cma_chiN     = sqrt(D)*(1-1/(4*D)+1/(21*D^2));
-
-            Aux.cma_cc    = (4+Aux.cma_betterN/D)/(4+D+2*Aux.cma_betterN/D);
-            Aux.cma_ccov  = 2/((D+1.3)^2+Aux.cma_betterN);
-            Aux.cma_cmu   = min(1-Aux.cma_ccov,2*(Aux.cma_betterN-2+1/Aux.cma_betterN)/((D+2)^2+2*Aux.cma_betterN/2));
-            Aux.cma_hth   = (1.4+2/(D+1))*Aux.cma_chiN;
-
-            Aux.cma_mean  = unifrnd(lower,upper);
-            Aux.cma_ps    = zeros(1,D);
-            Aux.cma_pc    = zeros(1,D);
-            Aux.cma_C     = eye(D);
-            Aux.cma_sigma = 0.1*(upper-lower);
-        end
-
-        % load parameters
-        C     = Aux.cma_C;
-        mean  = Aux.cma_mean;
-        sigma = Aux.cma_sigma;
-
-        % search
-        Disturb = zeros(N,D);
-        for i = 1:N
-            Disturb(i,:) = mvnrnd(zeros(1,D),C);
-        end
-        output1 = mean+sigma.*Disturb;
-        Aux.cma_Disturb = Disturb;
-        output2 = Aux;
-
-    case 'parameter'
-        % n/a
-
-    case 'behavior'
-        output1 = {'';'GS'}; % always performs global search
-
-    case 'algorithm'
-        Parent = varargin{1};
-        bound  = varargin{2};
-        Aux    = varargin{3};
-        lower  = bound(1,:);
-        upper  = bound(2,:);
-        [N,D] = size(Parent);
-
-        % initialize parameters
-        if ~isfield(Aux,'cma_halfN')
-            Aux.cma_halfN   = round(N/2);
-            w               = log(Aux.cma_halfN+0.5)-log(1:Aux.cma_halfN);
-            Aux.cma_w       = w./sum(w);
-            Aux.cma_betterN = 1/sum(Aux.cma_w.^2);
-
-            Aux.cma_csigma  = (Aux.cma_betterN+2)/(D+Aux.cma_betterN+5);
-            Aux.cma_dsigma  = Aux.cma_csigma+2*max(sqrt((Aux.cma_betterN-1)/(D+1))-1,0)+1;
-            Aux.cma_chiN     = sqrt(D)*(1-1/(4*D)+1/(21*D^2));
-
-            Aux.cma_cc    = (4+Aux.cma_betterN/D)/(4+D+2*Aux.cma_betterN/D);
-            Aux.cma_ccov  = 2/((D+1.3)^2+Aux.cma_betterN);
-            Aux.cma_cmu   = min(1-Aux.cma_ccov,2*(Aux.cma_betterN-2+1/Aux.cma_betterN)/((D+2)^2+2*Aux.cma_betterN/2));
-            Aux.cma_hth   = (1.4+2/(D+1))*Aux.cma_chiN;
-
-            Aux.cma_mean  = unifrnd(lower,upper);
-            Aux.cma_ps    = zeros(1,D);
-            Aux.cma_pc    = zeros(1,D);
-            Aux.cma_C     = eye(D);
-            Aux.cma_sigma = 0.1*(upper-lower);
-        end
-
-        % load parameters
-        C     = Aux.cma_C;
-        mean  = Aux.cma_mean;
-        sigma = Aux.cma_sigma;
-
-        % search
-        Disturb = zeros(N,D);
-        for i = 1:N
-            Disturb(i,:) = mvnrnd(zeros(1,D),C);
-        end
-        output1 = mean+sigma.*Disturb;
-        Aux.cma_Disturb = Disturb;
-        output2 = Aux;
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/search_de_current.m b/matlab/Components/search_de_current.m
deleted file mode 100644
index f0cc74f..0000000
--- a/matlab/Components/search_de_current.m
+++ /dev/null
@@ -1,61 +0,0 @@
-function [output1,output2] = search_de_current(varargin)
-% The "current/1" differential mutation.
-%------------------------------Reference-----------------------------------
-% Storn R, Price K. Differential evolution-a simple and efficient heuristic
-% for global optimization over continuous spaces[J]. Journal of Global 
-% Optimization, 1997, 11(4): 341-359.
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Parent = varargin{1};
-        Para   = varargin{3};
-        Aux    = varargin{4};
-
-        Parent = Parent.decs;
-        F  = Para(1);
-        CR = Para(2);      
-
-        [N,D]   = size(Parent);
-        Parent1 = Parent;
-        Parent2 = Parent(randperm(N),:);
-        Parent3 = Parent(randperm(N),:);
-
-        ind = rand(N,D) < CR;
-        Offspring = Parent;
-        Offspring(ind) = Parent1(ind) + F*(Parent2(ind)-Parent3(ind));
-        output1 = Offspring;
-        output2 = Aux;
-
-    case 'parameter'
-        output1 = [0,1;0,1]; % F and CR 
-
-    case 'behavior'
-        output1 = {'LS','small','small';'GS','large','large'}; % small F and CR values perform local search
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/search_de_current_best.m b/matlab/Components/search_de_current_best.m
deleted file mode 100644
index 3cf3b19..0000000
--- a/matlab/Components/search_de_current_best.m
+++ /dev/null
@@ -1,64 +0,0 @@
-function [output1,output2]  = search_de_current_best(varargin)
-% The "current-to-best/1" differential mutation.
-
-%------------------------------Reference-----------------------------------
-% Storn R, Price K. Differential evolution-a simple and efficient heuristic
-% for global optimization over continuous spaces[J]. Journal of Global 
-% Optimization, 1997, 11(4): 341-359.
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Parent = varargin{1};
-        Para   = varargin{3};
-        Aux    = varargin{4};
-
-        F        = Para(1);
-        CR       = Para(2);
-        [~,best] = min(Parent.fits);
-        GbestDec = Parent(best).dec; % the best solution
-        Parent   = Parent.decs;
-
-        [N,D]   = size(Parent);
-        Parent1 = Parent;
-        Parent2 = repmat(GbestDec,N,1);
-        Parent3 = Parent1(randperm(N),:);
-
-        ind = rand(N,D) < CR;
-        Offspring = Parent;
-        Offspring(ind) = Parent1(ind) + F*(Parent2(ind)-Parent3(ind));
-        output1 = Offspring;
-        output2 = Aux;
-
-    case 'parameter'
-        output1 = [0,1;0,1]; % F and CR
-
-    case 'behavior'
-        output1 = {'LS','small','small';'GS','large','large'}; % small F and CR values perform local search
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/search_de_random.m b/matlab/Components/search_de_random.m
deleted file mode 100644
index a5cebe8..0000000
--- a/matlab/Components/search_de_random.m
+++ /dev/null
@@ -1,62 +0,0 @@
-function [output1,output2] = search_de_random(varargin)
-% The "random/1" differential mutation.
-
-%------------------------------Reference-----------------------------------
-% Storn R, Price K. Differential evolution-a simple and efficient heuristic
-% for global optimization over continuous spaces[J]. Journal of Global 
-% Optimization, 1997, 11(4): 341-359.
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Parent = varargin{1};
-        Para   = varargin{3};
-        Aux    = varargin{4};
-
-        F       = Para(1);
-        CR      = Para(2);
-        Parent  = Parent.decs;
-
-        [N,D]   = size(Parent);
-        Parent1 = Parent(randperm(N),:);
-        Parent2 = Parent(randperm(N),:);
-        Parent3 = Parent(randperm(N),:);
-
-        ind = rand(N,D) < CR;
-        Offspring = Parent;
-        Offspring(ind) = Parent1(ind) + F*(Parent2(ind)-Parent3(ind));
-        output1 = Offspring;
-        output2 = Aux;
-
-    case 'parameter'
-        output1 = [0,1;0,1]; % F and CR
-
-    case 'behavior'
-        output1 = {'LS','small','small';'GS','large','large'}; % small F and CR values perform local search
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/search_eda.m b/matlab/Components/search_eda.m
deleted file mode 100644
index cdd8138..0000000
--- a/matlab/Components/search_eda.m
+++ /dev/null
@@ -1,56 +0,0 @@
-function [output1,output2] = search_eda(varargin)
-% The estimation of distribution.
-
-%------------------------------Reference-----------------------------------
-% Baluja S, Caruana R. Removing the genetics from the standard genetic 
-% algorithm[M]//Machine Learning Proceedings 1995. Morgan Kaufmann, 1995: 
-% 38-46.
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Parent = varargin{1};
-        Aux    = varargin{4};
-
-        Parent = Parent.decs;
-        [N,D]  = size(Parent);
-        
-        output1 = zeros(N,D);
-        for i = 1:D
-            pd = fitdist(Parent(:,i),'normal'); % fit a normal distribution
-            output1(:,i) = random(pd,[N,1]); % sample for the fitted distribution
-        end
-        output2 = Aux;
-  
-    case 'parameter'
-        % n/a
-
-    case 'behavior'
-        output1 = {'';'GS'}; % always performs global search
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/search_insert.m b/matlab/Components/search_insert.m
deleted file mode 100644
index 3265681..0000000
--- a/matlab/Components/search_insert.m
+++ /dev/null
@@ -1,61 +0,0 @@
-function [output1,output2] = search_insert(varargin)
-% Randomly select two elements, then insert the second element to the
-% position next (right side) to the first element.
-
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Solution = varargin{1};
-        Aux      = varargin{4};
-
-        if ~isnumeric(Solution)
-            New = Solution.decs;
-        else
-            New = Solution;
-        end
-        [N,D] = size(New);
-
-        for i = 1:N
-            CurrentNew = New(i,:);
-            k = randperm(D,2); % the two points particulated in insertion should be different
-            k = sort(k,'ascend');
-            Temp = CurrentNew(k(2));
-            CurrentNew(k(2)) = [];
-            New(i,:) = [CurrentNew(1:k(1)),Temp,CurrentNew(k(1)+1:end)];
-        end
-        output1 = New;
-        output2 = Aux;
-    
-    case 'parameter'
-        % n/a
-
-    case 'behavior'
-        output1 = {'LS';''}; % always perform local search
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/search_mu_cauchy.m b/matlab/Components/search_mu_cauchy.m
deleted file mode 100644
index 7b5a275..0000000
--- a/matlab/Components/search_mu_cauchy.m
+++ /dev/null
@@ -1,67 +0,0 @@
-function [output1,output2] = search_mu_cauchy(varargin)
-% The Cauchy mutation.
-%------------------------------Reference-----------------------------------
-% Yao X, Liu Y, Lin G. Evolutionary programming made faster[J]. IEEE 
-% Transactions on Evolutionary computation, 1999, 3(2): 82-102.
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Parent  = varargin{1};
-        Aux     = varargin{4};
-        innerG  = varargin{6};
-
-        if ~isnumeric(Parent)
-            Parent = Parent.decs;
-        end
-        [N,D] = size(Parent);
-        
-        % initialize eta
-        if innerG == 1
-            Aux.cauchy_eta = rand(N,D); 
-        end
-        
-        % search
-        Disturb = Aux.cauchy_eta.*trnd(1,N,D);
-        output1 = Parent+Disturb;
-
-        % update eta
-        tau1     = 1/sqrt(2*sqrt(D));
-        tau2     = 1/sqrt(2*D);
-        normal   = repmat(randn(N,1),1,D);
-        normal_j = randn(N,D);
-        Aux.cauchy_eta = Aux.cauchy_eta.*exp(tau2*normal+tau1*normal_j);
-        output2  = Aux;
-
-    case 'parameter'
-        % n/a
-
-    case 'behavior'
-        output1 = {'';'GS'}; % always performs global search
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/search_mu_gaussian.m b/matlab/Components/search_mu_gaussian.m
deleted file mode 100644
index 86e74da..0000000
--- a/matlab/Components/search_mu_gaussian.m
+++ /dev/null
@@ -1,69 +0,0 @@
-function [output1,output2] = search_mu_gaussian(varargin)
-% The Gaussian mutation.
-
-%------------------------------Reference-----------------------------------
-% Fogel D B. Artificial intelligence through simulated evolution[M]. 
-% Wiley-IEEE Press, 1998.
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Parent  = varargin{1};
-        Aux     = varargin{4};
-        innerG  = varargin{6};
-
-        if ~isnumeric(Parent)
-            Parent = Parent.decs;
-        end
-        [N,D] = size(Parent);
-        
-        % initialize eta
-        if innerG == 1
-            Aux.gaussian_eta = rand(N,D); 
-        end
-     
-        % search
-        Disturb = Aux.gaussian_eta.*randn(N,D);
-        output1 = Parent+Disturb;
-
-        % update eta
-        tau1      = 1/sqrt(2*sqrt(D));
-        tau2      = 1/sqrt(2*D);
-        normal    = repmat(randn(N,1),1,D);
-        normal_j  = randn(N,D);
-        Aux.gaussian_eta = Aux.gaussian_eta.*exp(tau2*normal+tau1*normal_j);
-        output2   = Aux;
-
-
-    case 'parameter'
-        % n/a
-
-    case 'behavior'
-        output1 = {'';'GS'}; % always performs global search
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/search_mu_polynomial.m b/matlab/Components/search_mu_polynomial.m
deleted file mode 100644
index ca44697..0000000
--- a/matlab/Components/search_mu_polynomial.m
+++ /dev/null
@@ -1,90 +0,0 @@
-function [output1,output2] = search_mu_polynomial(varargin)
-% The polynomial mutation.
-
-%------------------------------Reference-----------------------------------
-% Deb K, Goyal M. A combined genetic adaptive search (GeneAS) for 
-% engineering design[J]. Computer Science and Informatics, 1996, 26: 30-45.
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Parent  = varargin{1};
-        Problem = varargin{2};
-        Para    = varargin{3};
-        Aux     = varargin{4};
-        ProbM   = Para(1);
-        DisM    = Para(2);
-        
-        if ~isnumeric(Parent)
-            Offspring = Parent.decs;           
-        else
-            Offspring = Parent;
-        end    
-        [N,D] = size(Offspring);
-
-        Lower = repmat(Problem.bound(1,:),N,1);
-        Upper = repmat(Problem.bound(2,:),N,1);
-
-        Site  = rand(N,D) < ProbM;
-        mu    = rand(N,D);
-        temp  = Site & mu<=0.5;
-        Offspring(temp) = Offspring(temp)+(Upper(temp)-Lower(temp)).*((2.*mu(temp)+(1-2.*mu(temp)).*...
-            (1-(Offspring(temp)-Lower(temp))./(Upper(temp)-Lower(temp))).^(DisM+1)).^(1/(DisM+1))-1);
-        temp = Site & mu>0.5;
-        Offspring(temp) = Offspring(temp)+(Upper(temp)-Lower(temp)).*(1-(2.*(1-mu(temp))+2.*(mu(temp)-0.5).*...
-            (1-(Upper(temp)-Offspring(temp))./(Upper(temp)-Lower(temp))).^(DisM+1)).^(1/(DisM+1)));
-        output1 = Offspring;
-        output2 = Aux;
-    
-    case 'parameter'
-        output1 = [0,0.3;20,40]; % mutation probability and distribution
-
-    case 'behavior'
-        output1 = {'LS','small','large';'GS','large','small'}; % small probabilities and large distributions perform local search
-
-    case 'algorithm'
-        Parent    = varargin{1};
-        bound     = varargin{2};
-        ProbM     = 1;
-        DisM      = 30;
-        Offspring = Parent;
-        [N,D] = size(Offspring);
-
-        Lower = repmat(bound(1,:),N,1);
-        Upper = repmat(bound(2,:),N,1);
-        Site  = rand(N,D) < ProbM;
-        mu    = rand(N,D);
-        temp  = Site & mu<=0.5;
-        Offspring(temp) = Offspring(temp)+(Upper(temp)-Lower(temp)).*((2.*mu(temp)+(1-2.*mu(temp)).*...
-            (1-(Offspring(temp)-Lower(temp))./(Upper(temp)-Lower(temp))).^(DisM+1)).^(1/(DisM+1))-1);
-        temp = Site & mu>0.5;
-        Offspring(temp) = Offspring(temp)+(Upper(temp)-Lower(temp)).*(1-(2.*(1-mu(temp))+2.*(mu(temp)-0.5).*...
-            (1-(Upper(temp)-Offspring(temp))./(Upper(temp)-Lower(temp))).^(DisM+1)).^(1/(DisM+1)));
-        output1 = Offspring;
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/search_mu_uniform.m b/matlab/Components/search_mu_uniform.m
deleted file mode 100644
index 84af2a4..0000000
--- a/matlab/Components/search_mu_uniform.m
+++ /dev/null
@@ -1,59 +0,0 @@
-function [output1,output2] = search_mu_uniform(varargin)
-% The uniform mutation.
-
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Parent  = varargin{1};
-        Problem = varargin{2};
-        Para    = varargin{3};
-        Aux     = varargin{4};
-
-        if ~isnumeric(Parent)
-            Offspring = Parent.decs;
-        else
-            Offspring = Parent;
-        end
-        Prob  = Para;
-        [N,D] = size(Offspring);
-        
-        Lower = Problem.bound(1,:);
-        Upper = Problem.bound(2,:);
-        ind = rand(N,D) < Prob;
-        Temp = unifrnd(repmat(Lower,N,1),repmat(Upper,N,1));
-        Offspring(ind) = Temp(ind);
-        output1 = Offspring;
-        output2 = Aux;
-
-    case 'parameter'
-        output1 = [0,0.3]; % mutation probability 
-    case 'behavior'
-        output1 = {'LS','small';'GS','large'}; % small probabilities perform local search
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
diff --git a/matlab/Components/search_pso.m b/matlab/Components/search_pso.m
deleted file mode 100644
index ff218ce..0000000
--- a/matlab/Components/search_pso.m
+++ /dev/null
@@ -1,72 +0,0 @@
-function [output1,output2] = search_pso(varargin)
-% Particle swarm optimization's particle fly operator.
-
-%------------------------------Reference-----------------------------------
-% Shi Y, Eberhart R. A modified particle swarm optimizer[C]//1998 IEEE 
-% International Conference on Evolutionary Computation Proceedings. IEEE 
-% World Congress on Computational Intelligence (Cat. No. 98TH8360). IEEE, 
-% 1998: 69-73.
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Solution = varargin{1};
-        Para     = varargin{3};
-        Aux      = varargin{4};
-
-        [N,D] = size(Solution.decs);
-        W = Para; % inertia weight
-
-        % initialize pbest, gbest, and velocity
-        if ~isfield(Aux,'Pbest')
-            Aux.Pbest = Solution; % personal best solutions
-            Aux.Gbest = Solution(randi(N)); % global best solution
-            Aux.V = zeros(N,1);
-        end
-
-        % particle fly
-        Dec   = Solution.decs;
-        Pbest = Aux.Pbest.decs;
-        Gbest = Aux.Gbest.dec;
-
-        r1 = repmat(rand(N,1),1,D);
-        r2 = repmat(rand(N,1),1,D);
-        V  = W.*Aux.V+2*r1.*(Pbest-Dec)+2*r2.*(Gbest-Dec); % N*D
-        output1 = Dec+V; % N*D
-
-        % update volecity
-        Aux.V   = V;
-        output2 = Aux;
-
-    case 'parameter'
-        output1 = [0,0.5]; % inertia weight
-
-    case 'behavior'
-        output1 = {'';'GS'}; % always perform global search
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/search_reset_creep.m b/matlab/Components/search_reset_creep.m
deleted file mode 100644
index 580dd11..0000000
--- a/matlab/Components/search_reset_creep.m
+++ /dev/null
@@ -1,71 +0,0 @@
-function [output1,output2] = search_reset_creep(varargin)
-% Add a small value (positive or negative) to each element of solutions
-% with a probability, for discrete problems with ordinal attributes.
-
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Solution = varargin{1};
-        Problem  = varargin{2};
-        Para     = varargin{3};
-        Aux      = varargin{4};
-
-        Prob = Para(1);
-        Amp  = Para(2);
-        Amp  = max(1,round((Problem.bound(2,:)-Problem.bound(1,:)+1).*Amp)); % 1*D
-        if ~isnumeric(Solution)
-            New = Solution.decs;
-        else
-            New = Solution;
-        end
-        [N,D] = size(New);
-
-        StepSize = zeros(N,D);
-        for i = 1:D
-            StepSize(:,i) = randi(Amp(i),N,1); % sample step size for each dimension, N*D
-        end
-        ind = rand(N,D) < 0.5; % N*D logical indices
-        StepSize(ind) = -StepSize(ind);
-        Temp = New+StepSize;
-        Lower = repmat(Problem.bound(1,:),N,1);
-        Upper = repmat(Problem.bound(2,:),N,1);
-        Temp = max(min(Temp,Upper),Lower);
-
-        ind = rand(N,D) < Prob; % N*D
-        New(ind) = Temp(ind);
-        output1 = New;
-        output2 = Aux;
-
-    case 'parameter'
-        output1 = [0,0.5;0,0.5]; % probability and amplitude 
-
-    case 'behavior'
-        output1 = {'LS','small','small';'GS','large','large'}; % small probabilities and amplitudes perform local search
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/search_reset_n.m b/matlab/Components/search_reset_n.m
deleted file mode 100644
index 59dd184..0000000
--- a/matlab/Components/search_reset_n.m
+++ /dev/null
@@ -1,74 +0,0 @@
-function [output1,output2] = search_reset_n(varargin)
-% Randomly select n elements of each solution and reset them to random
-% values.
-
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the
-% Free Software Foundation, either version 3 of the License, or any later
-% version.
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Solution = varargin{1};
-        Problem  = varargin{2};
-        Para     = varargin{3};
-        Aux      = varargin{4};
-
-        if ~isnumeric(Solution)
-            New = Solution.decs;
-        else
-            New = Solution;
-        end
-
-        n = max(round(Para),1);
-        [N,D] = size(New);
-
-        for i = 1:N
-            ind = randperm(D,n);
-            curr = New(i,ind);
-            for j = 1:n
-                while New(i,ind(j)) == curr(j)
-                    New(i,ind(j)) = randi([Problem.bound(1,ind(j)),Problem.bound(2,ind(j))]);
-                end
-            end
-        end
-        output1 = New;
-        output2 = Aux;
-
-    case 'parameter'
-        % number of problem's decision variable
-        Problem  = varargin{1};
-        D = zeros(length(Problem),1);
-        for i = 1:length(Problem)
-            D(i) = size(Problem(i).bound,2);
-        end
-        D = min(D);
-        n_max = D; % the maximum n
-        output1 = [1,n_max];
-
-    case 'behavior'
-        output1 = {'LS','small';'GS','large'}; % small n values perform local search
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/search_reset_one.m b/matlab/Components/search_reset_one.m
deleted file mode 100644
index 12c9624..0000000
--- a/matlab/Components/search_reset_one.m
+++ /dev/null
@@ -1,62 +0,0 @@
-function [output1,output2] = search_reset_one(varargin)
-% Randomly select an element of each solution and reset it to a random
-% value.
-
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Solution = varargin{1};
-        Problem  = varargin{2};
-        Aux      = varargin{4};
-
-        if ~isnumeric(Solution)
-            New = Solution.decs;
-        else
-            New = Solution;
-        end
-
-        [N,D] = size(New);
-        ind = randi(D,N,1); % N*1
-        
-        for i = 1:N
-            curr = New(i,ind(i));
-            while New(i,ind(i)) == curr
-                New(i,ind(i)) = randi([Problem.bound(1,ind(i)),Problem.bound(2,ind(i))]);
-            end
-        end
-        output1 = New;
-        output2 = Aux;
-
-    case 'parameter'
-        % n/a
-
-    case 'behavior'
-        output1 = {'LS';''}; % always perform local search
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/search_reset_rand.m b/matlab/Components/search_reset_rand.m
deleted file mode 100644
index a39a140..0000000
--- a/matlab/Components/search_reset_rand.m
+++ /dev/null
@@ -1,62 +0,0 @@
-function [output1,output2] = search_reset_rand(varargin)
-% Reset each element of solutions to a random value with a probability.
-
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Solution = varargin{1};
-        Problem  = varargin{2};
-        Para     = varargin{3};
-        Aux      = varargin{4};
-
-        Prob = Para;
-        if ~isnumeric(Solution)
-            New = Solution.decs;
-        else
-            New = Solution;
-        end
-        [N,D] = size(New);
-
-        Temp = zeros(N,D);
-        for j = 1:D
-            Temp(:,j) = randi([Problem.bound(1,j),Problem.bound(2,j)],N,1);
-        end
-
-        ind = rand(N,D) < Prob; % N*D
-        New(ind) = Temp(ind);
-        output1 = New;
-        output2 = Aux;
-
-    case 'parameter'
-        output1 = [0.05,0.5]; % reset probability 
-
-    case 'behavior'
-        output1 = {'LS','small';'GS','large'}; % samll probabilities perform local search
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/search_scramble.m b/matlab/Components/search_scramble.m
deleted file mode 100644
index 43363ea..0000000
--- a/matlab/Components/search_scramble.m
+++ /dev/null
@@ -1,63 +0,0 @@
-function [output1,output2] = search_scramble(varargin)
-% Scramble all the elements between two randomly selected indices of each
-% solution.
-
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Solution = varargin{1};
-        Aux      = varargin{4};
-
-        if ~isnumeric(Solution)
-            New = Solution.decs;
-        else
-            New = Solution;
-        end
-        [N,D] = size(New);
-
-        for i = 1:N
-            k = randperm(D,2);
-            k = sort(k);
-            n = k(2)-k(1)+1; % number of elements to be scarmbled
-
-            Temp = New(i,k(1):k(2));
-            ind = randperm(n);
-            Temp = Temp(ind);
-            New(i,k(1):k(2)) = Temp;
-        end
-        output1 = New;
-        output2 = Aux;
-
-    case 'parameter'
-        % n/a
-
-    case 'behavior'
-        output1 = {'';'GS'}; % always perform global search
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/search_swap.m b/matlab/Components/search_swap.m
deleted file mode 100644
index 70d412f..0000000
--- a/matlab/Components/search_swap.m
+++ /dev/null
@@ -1,56 +0,0 @@
-function [output1,output2] = search_swap(varargin)
-% Swap two randomly selected elements.
-
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Solution = varargin{1};
-        Aux      = varargin{4};
-
-        if ~isnumeric(Solution)
-            Solution = Solution.decs;
-        end      
-        [N,D] = size(Solution);
-        New = Solution;
-
-        for i = 1:N
-            k = randperm(D,2);
-            New(i,k(1)) = Solution(i,k(2));
-            New(i,k(2)) = Solution(i,k(1));
-        end
-        output1 = New;
-        output2 = Aux;
-
-    case 'parameter'
-        % n/a
-
-    case 'behavior'
-        output1 = {'LS';''}; % always perform local search
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/search_swap_multi.m b/matlab/Components/search_swap_multi.m
deleted file mode 100644
index 7e40256..0000000
--- a/matlab/Components/search_swap_multi.m
+++ /dev/null
@@ -1,61 +0,0 @@
-function [output1,output2] = search_swap_multi(varargin)
-% Swap each pair of elements between two randomly selected indices.
-
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Solution = varargin{1};
-        Aux      = varargin{4};
-
-        if ~isnumeric(Solution)
-            Solution = Solution.decs;
-        end
-        [N,D] = size(Solution);
-        New = Solution;
-
-        for i = 1:N
-            k = randperm(D,2);
-            k = sort(k);
-            n = floor((k(2)-k(1)+1)/2); % number of pairs of elements to be swapped
-
-            for j = 1:n
-                New(i,k(1)+j-1) = Solution(i,k(2)-j+1);
-                New(i,k(2)-j+1) = Solution(i,k(1)+j-1);
-            end
-        end
-        output1 = New;
-        output2 = Aux;
-
-    case 'parameter'
-        % n/a
-
-    case 'behavior'
-        output1 = {'';'GS'}; % always perform global search
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/update_always.m b/matlab/Components/update_always.m
deleted file mode 100644
index 2d1ef10..0000000
--- a/matlab/Components/update_always.m
+++ /dev/null
@@ -1,44 +0,0 @@
-function [output1,output2] = update_always(varargin)
-% Always select newly generated solutions.
-
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Solution = varargin{1};
-        Problem  = varargin{2};
-
-        output1 = Solution(end-Problem.N+1:end);
-
-    case 'parameter'
-        % n/a
-
-    case 'behavior'
-        output1 = {'';''}; 
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/update_greedy.m b/matlab/Components/update_greedy.m
deleted file mode 100644
index d6327ef..0000000
--- a/matlab/Components/update_greedy.m
+++ /dev/null
@@ -1,45 +0,0 @@
-function [output1,output2] = update_greedy(varargin)
-% Select the best (in terms of fitness) N solutions.
-
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Solution = varargin{1};
-        Problem  = varargin{2};
-
-        [~,ind]  = sort(Solution.fits,'ascend');
-        output1 = Solution(ind(1:Problem.N));
-
-    case 'parameter'
-        % n/a
-
-    case 'behavior'
-        output1 = {'';''}; 
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/update_pairwise.m b/matlab/Components/update_pairwise.m
deleted file mode 100644
index 68bcc16..0000000
--- a/matlab/Components/update_pairwise.m
+++ /dev/null
@@ -1,53 +0,0 @@
-function [output1,output2] = update_pairwise(varargin)
-% Select the better solution from each pair of old and new solutions.
-
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Solution = varargin{1};
-
-        Fitness  = Solution.fits;
-        tempN    = size(Fitness,1)/2;
-        Old      = Fitness(1:tempN);
-        New      = Fitness(tempN+1:end);
-
-        compare1 = Old<=New;
-        compare2 = Old>New;
-
-        ind      = 1:length(Solution);
-        ind      = ind([compare1;compare2]);
-        output1  = Solution(ind);
-
-    case 'parameter'
-        % n/a
-
-    case 'behavior'
-        output1 = {'';''}; 
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/update_round_robin.m b/matlab/Components/update_round_robin.m
deleted file mode 100644
index 8dfcc55..0000000
--- a/matlab/Components/update_round_robin.m
+++ /dev/null
@@ -1,53 +0,0 @@
-function [output1,output2] = update_round_robin(varargin)
-% Conduct update via round-robin tournament.
-
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Solution = varargin{1};
-        Problem  = varargin{2};
-
-        Fitness  = Solution.fits;
-        K   = min(10,Problem.N-1);
-        ind = 1:length(Solution);
-        win = zeros(length(Solution),1);
-        for i = 1:length(Solution)
-            win(i) = sum(Fitness(i)<=Fitness(randperm(end,K)));
-        end
-        [~,rank] = sort(win,'descend');
-        ind = ind(rank(1:Problem.N));
-        output1 = Solution(ind);
-
-    case 'parameter'
-        % n/a
-
-    case 'behavior'
-        output1 = {'';''}; 
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Components/update_simulated_annealing.m b/matlab/Components/update_simulated_annealing.m
deleted file mode 100644
index 118a543..0000000
--- a/matlab/Components/update_simulated_annealing.m
+++ /dev/null
@@ -1,61 +0,0 @@
-function [output1,output2] = update_simulated_annealing(varargin)
-% Simulated annealing's update mechanism, i.e., accept worse soluion with a
-% probability.
-
-%------------------------------Reference-----------------------------------
-% Kirkpatrick S, Gelatt Jr C D, Vecchi M P. Optimization by simulated 
-% annealing[J]. Science, 1983, 220(4598): 671-680.
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-mode = varargin{end};
-switch mode
-    case 'execute'
-        Solution = varargin{1};
-        Problem  = varargin{2};
-        Para     = varargin{3};
-        G        = varargin{5};
-
-        T_initial = Para; % initial temperture
-        T_final   = 0.01; % final temperture
-        Rate      = nthroot(T_final/T_initial,Problem.Gmax); % temperture decrease rate
-        T         = T_initial*Rate^G; % current temperture
-
-        Old = Solution(1:Problem.N);
-        New = Solution(Problem.N+1:end);
-      
-        accept = rand(Problem.N,1) < exp((Old.fits-New.fits)./abs(Old.fits+1e-6)./T); % accept worse solutions by annealing
-        accept(Old.fits>New.fits) = true; % always accept better solutions      
-        Old(accept) = New(accept);
-
-        output1 = Old;
-
-    case 'parameter'
-        output1 = [0.1,1]; % initial temperture 
-
-    case 'behavior'
-        output1 = {'';''}; 
-end
-
-if ~exist('output1','var')
-    output1 = [];
-end
-if ~exist('output2','var')
-    output2 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/IOH_Test.py b/matlab/IOH_Test.py
deleted file mode 100644
index c00f5b3..0000000
--- a/matlab/IOH_Test.py
+++ /dev/null
@@ -1,198 +0,0 @@
-import os
-
-import ioh
-import numpy
-import numpy as np
-import array
-
-import pandas as pd
-
-def test(x0):
-    print('go!')
-    x0 = np.array(x0)
-    # In order to instantiate a problem instance, we can do the following:
-    problem = ioh.get_problem(
-        "Sphere",
-        instance=1,
-        dimension=10,
-        problem_class=ioh.ProblemClass.REAL
-    )
-    # We can access the contraint information of the problem
-    #x0 = np.random.uniform(problem.bounds.lb, problem.bounds.ub,[10,10])
-
-
-    # Evaluation happens like a 'normal' objective function would
-    return problem(x0)
-
-def PBO(x0,length,problem_id):
-    #print("test")
-    #print(int(problem_id))
-    # In order to instantiate a problem instance, we can do the following:
-    problem = ioh.get_problem(
-        int(problem_id),
-        instance=1,
-        dimension=int(length),
-        problem_class=ioh.ProblemClass.PBO
-    )
-
-    problem.enforce_bounds(how=ioh.ConstraintEnforcement.SOFT, weight=1.0, exponent=1.0)
-
-    # We can access the contraint information of the problem
-    population = np.array(x0).astype(int)
-    #print(population)
-    # Evaluation happens like a 'normal' objective function would
-    res = problem(population)
-    ret = array.array('d', res)
-    return ret
-
-def tabu_pbo(x0, length, problem_id):
-    #print("test")
-    #print(int(problem_id))
-    # In order to instantiate a problem instance, we can do the following:
-    problem = ioh.get_problem(
-        int(problem_id),
-        instance=1,
-        dimension=int(length),
-        problem_class=ioh.ProblemClass.PBO
-    )
-
-    problem.enforce_bounds(how=ioh.ConstraintEnforcement.SOFT, weight=1.0, exponent=1.0)
-
-    # We can access the contraint information of the problem
-    population = np.array(x0).astype(int)
-    #print(population)
-    # Evaluation happens like a 'normal' objective function would
-    res = problem(population)
-    return res
-
-def BBOB(x0,length,problem_id):
-    #print("test")
-    #print(int(problem_id))
-    #print(length)
-    # In order to instantiate a problem instance, we can do the following:
-    problem = ioh.get_problem(
-        int(problem_id),
-        instance=1,
-        dimension=int(length),
-        problem_class=ioh.ProblemClass.BBOB
-    )
-
-    #problem.enforce_bounds(how=ioh.ConstraintEnforcement.SOFT, weight=1.0, exponent=1.0)
-
-    # We can access the contraint information of the problem
-    population = np.array(x0).astype(float)
-    #print(population)
-    # Evaluation happens like a 'normal' objective function would
-    res = problem(population)
-    #print(res)
-    ret = array.array('d', res)
-    return ret
-def cosntrains():
-    # We take the Shere function as example
-    p = ioh.get_problem("Sphere", 1, 2)
-    # There a several strategies that modify a constraint's behavoir:
-    types = (
-        ioh.ConstraintEnforcement.NOT,       # Don't calulate the constraint function
-        ioh.ConstraintEnforcement.HIDDEN,    # Calulate the constraint, but don't penalize
-        ioh.ConstraintEnforcement.SOFT,      # Calculate both constraint and objective function value y, and penalize as y + p
-        ioh.ConstraintEnforcement.HARD,      # Calulate the constraint, if there is violation, don't calculate y, and only return p
-    )
-    for strategy in types:
-        p.enforce_bounds(how=strategy, weight=1.0, exponent=1.0)
-        print(strategy, p([7, 7]), p.constraints.violation())
-def test():
-    problem = ioh.get_problem(
-        1,
-        instance=1,
-        dimension=2,
-        problem_class=ioh.ProblemClass.BBOB
-    )
-
-    #problem.enforce_bounds(how=ioh.ConstraintEnforcement.SOFT, weight=1.0, exponent=1.0)
-
-    while(problem.state.optimum_found!=True):
-        xO = np.random.random(size=[1000,2])*10-5
-        xO = [-1.63514,1.14487]
-        #res = BBOB(xO, 2, 1)
-        res = problem(xO)
-        print(res)
-        #print(problem.state.current_best)
-        if problem.state.optimum_found ==True:
-            print(res)
-            break
-def checkoptimum():
-    #path =os.getcwd()
-    #print(os.path.abspath(os.path.dirname(os.getcwd())))
-    f = open(os.path.dirname(os.getcwd()) + '/result/bbob_optimun' + '.txt', 'a')
-
-    for i in range(1,25):
-        for j in [2,5,10,20]:
-            problem = ioh.get_problem(
-                i,
-                instance=1,
-                dimension=j,
-                problem_class=ioh.ProblemClass.BBOB
-            )
-            print('id ={},dim={},optimum={} \n'.format(i,j,problem.optimum))
-            f.write('id ={},dim={},optimum={} \n'.format(i,j,problem.optimum))
-
-    f.close()
-
-def tabu_search(tabu_list_length,tabu_list_cycle,problem_id,dim,problem_fe):
-    best = np.random.randint(0,2,dim)
-    best_fit = tabu_pbo(best, dim, problem_id)
-    cur_problem_fe = 0
-    tabu_list =  [0] * tabu_list_length
-    while(cur_problem_fe<=problem_fe):
-        neighbors = build_neighbors(best,tabu_list)
-        fitness = tabu_pbo(neighbors, dim, problem_id)
-        if max(fitness) > best_fit:
-            best_fit = max(fitness)
-            best = neighbors[fitness.index(best_fit)]
-        for i in range(len(tabu_list)):
-            if tabu_list[i]>0:
-                tabu_list[i] -= 1
-        tabu_list[fitness.index(max(fitness))] = tabu_list_cycle
-        cur_problem_fe = cur_problem_fe + len(neighbors)
-    print('problem{1},best fitness {0} '.format(best_fit,problem_id))
-    return  best_fit
-
-def build_neighbors(X0,tabu_list):
-    neighbors = []
-    for i in range(X0.shape[0]):
-        if tabu_list[i]==0:
-            X_temp = X0.copy()
-            X_temp[i] = int(not(X_temp[i]))
-            neighbors.append(X_temp)
-    return neighbors
-# xO = np.random.randint(0,2,64)
-# length = 64
-# PBO(xO,length,2)
-#test()
-#checkoptimum()
-def run_tabu():
-    res = np.zeros((30, 23))
-    for j in range(30):
-        for i in range(1,24):
-            res[j,i-1] = tabu_search(625,3,i,625,50000)
-
-    column_means = np.mean(res, axis=0)
-
-    # 计算每行的方差
-    column_variances = np.var(res, axis=0)
-    write_excel(column_means,'Tabu_mean')
-    write_excel(column_variances, 'Tabu_var')
-    print("tsa")
-
-def write_excel(res,shell,file_path):
-    res = res.reshape(1,-1)
-    # 将 NumPy 数组转换为 pandas 数据框架
-    df = pd.DataFrame(res)
-
-    # 打开现有的 Excel 文件
-    existing_data = pd.read_excel(file_path)
-
-    # 将新数据追加到现有数据后面
-    with pd.ExcelWriter(file_path, engine='openpyxl', mode='a') as writer:
-        df.to_excel(writer, sheet_name=shell, index=False,header=False)
-#run_tabu()
\ No newline at end of file
diff --git a/matlab/Main/Agent.mat b/matlab/Main/Agent.mat
deleted file mode 100644
index 1731a77..0000000
Binary files a/matlab/Main/Agent.mat and /dev/null differ
diff --git a/matlab/Main/CEC2005F1.m b/matlab/Main/CEC2005F1.m
deleted file mode 100644
index 66a3c22..0000000
--- a/matlab/Main/CEC2005F1.m
+++ /dev/null
@@ -1,7 +0,0 @@
-function [outputArg1] = CEC2005F1(inputArg1)
-%CEC2005F1 此处显示有关此函数的摘要
-%   此处显示详细说明
-
-outputArg1 = sum(inputArg1.^2);
-end
-
diff --git a/matlab/Main/DeterministicActor.m b/matlab/Main/DeterministicActor.m
deleted file mode 100644
index d1dfce7..0000000
--- a/matlab/Main/DeterministicActor.m
+++ /dev/null
@@ -1,21 +0,0 @@
-actorNet =[ 
-    featureInputLayer(15,Name="comPathIn")
-    selfAttentionLayer(5,15)
-    layerNormalizationLayer
-    fullyConnectedLayer(15)
-    reluLayer;
-    scalingLayer(Name="meanPathOut",Scale=ActionInfo.UpperLimit)];
-
-
-actorNet = dlnetwork(actorNet);
-summary(actorNet)
-%rlContinuousGaussianActor
-
-actor = rlContinuousDeterministicActor(actorNet1, ObservationInfo, ActionInfo);
-actor.UseDevice = "gpu";
-
-% test
-result = getAction(actor,{rand(ObservationInfo.Dimension)});
-x = rand(15,1);
-dlx = dlarray(x,'CB');
-result = forward(actorNet1,dlx);%}
\ No newline at end of file
diff --git a/matlab/Main/Gaussian.m b/matlab/Main/Gaussian.m
deleted file mode 100644
index d3d7566..0000000
--- a/matlab/Main/Gaussian.m
+++ /dev/null
@@ -1,3 +0,0 @@
-function [y] = Gaussian(x,mu,sigma)
-y = 1/(sqrt(2*pi)*sigma)*exp(-(x-mu).^2/(2*sigma^2));
-end
diff --git a/matlab/Main/Positional_Encoding.m b/matlab/Main/Positional_Encoding.m
deleted file mode 100644
index 0901b1a..0000000
--- a/matlab/Main/Positional_Encoding.m
+++ /dev/null
@@ -1,16 +0,0 @@
-function [out] = Positional_Encoding(X)
-%POSITIONAL_ENCODING 此处显示有关此函数的摘要
-%   此处显示详细说明
-
-length = size(X,1);
-
-for pos = 0:length-1
-    if mod(pos,2)==1
-        X(pos+1,1) = X(pos+1,1) + cos(length/(10000^((pos-1)/length)));
-    else
-        X(pos+1,1) = X(pos+1,1) + sin(length/(10000^(pos/length)));
-    end
-end
-out = X;
-end
-
diff --git a/matlab/Main/ShowRes.m b/matlab/Main/ShowRes.m
deleted file mode 100644
index 6d2e887..0000000
--- a/matlab/Main/ShowRes.m
+++ /dev/null
@@ -1,13 +0,0 @@
-load('20230724T151736_episode_record.mat')
-
-performance = [episode_record.performance];
-
-reward = [episode_record.reward];
-
-X = size(reward,2);
-
-X = 1:X;
-
-plot(X,performance,X,reward)
-
-plot(performance)
\ No newline at end of file
diff --git a/matlab/Main/Test.m b/matlab/Main/Test.m
deleted file mode 100644
index 0d3a935..0000000
--- a/matlab/Main/Test.m
+++ /dev/null
@@ -1,6 +0,0 @@
-input = [1;1;1;1;1;1;1;1;1;1;1;1;1;1;1];
-
-test = [1,2,3,4];
-test1 = [5,6,7];
-test2 =[test,test1];
-input = Positional_Encoding(input);
\ No newline at end of file
diff --git a/matlab/Main/Train.m b/matlab/Main/Train.m
deleted file mode 100644
index 2c8c484..0000000
--- a/matlab/Main/Train.m
+++ /dev/null
@@ -1,76 +0,0 @@
-global Problem Data Setting seedTrain;
-
-global episode_record best_action best_p;
-
-global episode_ite;
-
-global times;
-
-times = datestr(now,30);
-
-episode_ite =0;
-episode_record = struct;
-best_p = 0;
-rng(0);
-
-[Problem,Data,Setting,seedTrain] = opt_env('Mode','design','Problem','CEC2005_f1','InstanceTrain',[1,2],'InstanceTest',3);
-
-% get env
-env = env();
-
-% get net
-ObservationInfo = getObservationInfo(env);
-ActionInfo = getActionInfo(env);
-
-% bug here? 等号右侧的输出数目不足，不满足赋值要求。
-[critic,actor] = net(ObservationInfo,ActionInfo);
-
-
-% set agent parameters
-criticOptions = rlOptimizerOptions( ...
-    LearnRate=1e-3, ...
-    GradientThreshold=1);
-actorOptions = rlOptimizerOptions( ...
-    LearnRate=2e-4, ...
-    GradientThreshold=1);
-
-agentOpts = rlPPOAgentOptions(...
-    SampleTime=-1,...
-    ActorOptimizerOptions=actorOptions,...
-    CriticOptimizerOptions=criticOptions,...
-    ExperienceHorizon=200,...
-    ClipFactor=0.2,... 
-    EntropyLossWeight=0.01,...
-    MiniBatchSize=64,...
-    NumEpoch=3,...
-    AdvantageEstimateMethod="gae",...
-    GAEFactor=0.95,...
-    DiscountFactor=0.998);
-
-
-agent = rlPPOAgent(actor,critic,agentOpts);
-
-
-% Test
-%getAction(agent,{rand(ObservationInfo.Dimension)});
-
-% train parameters
-trainOpts = rlTrainingOptions(...
-    MaxEpisodes=100000,...
-    MaxStepsPerEpisode=1,...
-    ScoreAveragingWindowLength=10,...
-    Plots="training-progress",...
-    StopTrainingCriteria="AverageReward",...
-    StopTrainingValue=10000);
-
-doTraining = true;
-
-
-if doTraining
-    trainingStats = train(agent,env,trainOpts);
-    save([times ,'_Agent.mat'],'agent');
-    save([times ,'_best_action.mat'],'best_action');
-    save([times ,'_episode_record.mat'],'episode_record');
-else
-    load('Agent.mat','agent');
-end
\ No newline at end of file
diff --git a/matlab/Main/env.m b/matlab/Main/env.m
deleted file mode 100644
index e6fcf74..0000000
--- a/matlab/Main/env.m
+++ /dev/null
@@ -1,17 +0,0 @@
-function [env] = env()
-%ENV creat env
-%   此处显示详细说明
-
-ObservationInfo = rlNumericSpec([15 1]);
-ObservationInfo.Name = "States";
-ObservationInfo.Description = 'done';
-
-% continue 15 dim action
-ActionInfo = rlNumericSpec([15 1]);
-ActionInfo.Name = "Action";
-ActionInfo.LowerLimit=0;
-ActionInfo.UpperLimit=1;
-
-env = rlFunctionEnv(ObservationInfo,ActionInfo,"myStepFunction","myResetFunction");
-end
-
diff --git a/matlab/Main/evaluate.m b/matlab/Main/evaluate.m
deleted file mode 100644
index 51eaa57..0000000
--- a/matlab/Main/evaluate.m
+++ /dev/null
@@ -1,13 +0,0 @@
-load('Agent.mat','agent');
-input = [1;1;1;1;1;1;1;1;1;1;1;1;1;1;1];
-res = zeros(1,1000);
-for i =1:1000
-    action = getAction(agent,input);
-    action = action{1};
-    action = sigmoid(action);
-    act1 = action(1:7,:);
-    act2 = action(9:15,:);
-    res(i) = sum(act1)-sum(act2);
-end
-plot(res)
-mean(res)
diff --git a/matlab/Main/myResetFunction.m b/matlab/Main/myResetFunction.m
deleted file mode 100644
index b13a86e..0000000
--- a/matlab/Main/myResetFunction.m
+++ /dev/null
@@ -1,9 +0,0 @@
-function [InitialObservation, LoggedSignal] = myResetFunction()
-% Reset function to place custom  environment into a random
-% initial state.
-
-LoggedSignal.State = [1;1;1;1;1;1;1;1;1;1;1;1;1;1;1];
-%LoggedSignal.State = [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1];
-InitialObservation = LoggedSignal.State;
-
-end
diff --git a/matlab/Main/myStepFunction.m b/matlab/Main/myStepFunction.m
deleted file mode 100644
index 5cad4d3..0000000
--- a/matlab/Main/myStepFunction.m
+++ /dev/null
@@ -1,60 +0,0 @@
-function [NextObs,Reward,IsDone,LoggedSignals] = myStepFunction(Action,LoggedSignals)
-
-global Problem Data Setting seedTrain;
-global episode_record best_action best_p;
-global episode_ite;
-global times;
-
-episode_ite = episode_ite +1;
-
-Action = sigmoid(Action);
-IsDone = true;
-
-obj = DESIGN;
-Setting.Action = Action;
-[performance Algs change] = obj.Get_performance(Problem,Data,Setting,seedTrain);
-%performance = Get_p('Mode','design','Problem','CEC2005_f1','InstanceTrain',[1,2],'InstanceTest',3);
-
-baseline =0;
-
-if episode_ite>10
-    per = [episode_record.performance];
-    per = per(end-9:end);
-    rew =0;
-    for i=1:10
-        rew = rew + per_to_rew(per(i));
-    end
-    
-    baseline = rew/10;
-end
-
-
-Reward = per_to_rew(performance);
-Reward =Reward -baseline - change*10;
-
-
-episode_record(episode_ite).performance = performance;
-episode_record(episode_ite).reward =Reward;
-episode_record(episode_ite).Algs = Algs;
-
-per = [episode_record.performance];
-min_p = min(per);
-
-if performance<=min_p
-    episode_ite
-    performance
-    best_action = Action;
-    Reward = Reward*1.5;
-    save([times ,'_episode_record.mat'],'episode_record');
-end
-
-
-LoggedSignals.State = [1;1;1;1;1;1;1;1;1;1;1;1;1;1;1];
-NextObs = LoggedSignals.State;
-
-end
-
-function [reward] = per_to_rew(performance)
-    distance = performance - (-450);
-    reward = ((1/distance)*450 - 1)*10;
-end
\ No newline at end of file
diff --git a/matlab/Main/net.m b/matlab/Main/net.m
deleted file mode 100644
index cabc9a7..0000000
--- a/matlab/Main/net.m
+++ /dev/null
@@ -1,77 +0,0 @@
-function [critic,actor] = net(ObservationInfo,ActionInfo)
-%NET 此处显示有关此函数的摘要
-%   此处显示详细说明
-
-% actor
-% Define common input path layer
-commonPath = [ 
-    featureInputLayer(15,Name="comPathIn")
-    selfAttentionLayer(5,15)
-    layerNormalizationLayer
-    fullyConnectedLayer(15,'WeightsInitializer','orthogonal')
-    tanhLayer(Name="comPathOut");];
-
-% Define mean value path
-meanPath = [
-    fullyConnectedLayer(15,'WeightsInitializer','orthogonal',Name="meanPathIn")
-    tanhLayer
-    fullyConnectedLayer(prod(ActionInfo.Dimension),'WeightsInitializer','orthogonal',Name="meanPathOut");
-    ];
-
-% Define standard deviation path
-sdevPath = [
-    fullyConnectedLayer(15,'WeightsInitializer','orthogonal',"Name","stdPathIn")
-    tanhLayer
-    fullyConnectedLayer(prod(ActionInfo.Dimension),'WeightsInitializer','orthogonal');
-    softplusLayer(Name="stdPathOut") 
-    ];
-
-% Add layers to layerGraph object
-actorNet = layerGraph(commonPath);
-actorNet = addLayers(actorNet,meanPath);
-actorNet = addLayers(actorNet,sdevPath);
-
-% Connect paths
-actorNet = connectLayers(actorNet,"comPathOut","meanPathIn/in");
-actorNet = connectLayers(actorNet,"comPathOut","stdPathIn/in");
-
-
-actorNet = dlnetwork(actorNet);
-summary(actorNet)
-%plot(actorNet)
-
-actor = rlContinuousGaussianActor(actorNet, ObservationInfo, ActionInfo, ...
-    "ActionMeanOutputNames","meanPathOut",...
-    "ActionStandardDeviationOutputNames","stdPathOut",...
-    ObservationInputNames="comPathIn");
-
-actor.UseDevice = "gpu";
-
-%test
-%x = rand(15,1);
-%dlx = dlarray(x,'CB');
-%[comPathOut meanPathOut stdPathOut] = forward(actorNet,dlx,Outputs=["comPathOut" "meanPathOut" "stdPathOut"])
-
-
-% critic  
-criticNet = [
-    featureInputLayer(15)
-    fullyConnectedLayer(50)
-    tanhLayer
-    fullyConnectedLayer(25)
-    tanhLayer
-    fullyConnectedLayer(1)];
-
-criticNet = dlnetwork(criticNet);
-summary(criticNet)
-%plot(criticNet)
-
-critic = rlValueFunction(criticNet,ObservationInfo);
-critic.UseDevice = "gpu";
-
-
-%test
-%getValue(critic,{rand(ObservationInfo.Dimension)})
-
-end
-
diff --git a/matlab/Main/opt_env.m b/matlab/Main/opt_env.m
deleted file mode 100644
index 00ded0b..0000000
--- a/matlab/Main/opt_env.m
+++ /dev/null
@@ -1,78 +0,0 @@
-function [Problem,Data,Setting,seedTrain] = opt_env(varargin)
-% Set the AutoOpt software for algorithm evaluation
-
-% get mode
-if any(strcmp(varargin,'Mode'))
-    Setting = struct;
-    ind = find(strcmp(varargin,'Mode'));
-    Setting.Mode = varargin{ind+1};
-else
-    error('Please set the mode to "design" or "solve".');
-end
-
-% get problem
-if strcmp(Setting.Mode,'design')
-    [prob,instanceTrain,instanceTest] = Input(varargin,Setting,'data');
-elseif strcmp(Setting.Mode,'solve')
-    [prob,~] = Input(varargin,Setting,'data');
-end
-
-% get problem id
-if any(strcmp(varargin,'Problem_id'))
-    ind = find(strcmp(varargin,'Problem_id'));
-    Setting.Problem_id = varargin{ind+1};
-end
-
-if any(strcmp(varargin,'eval'))
-    ind = find(strcmp(varargin,'eval'));
-    Setting.eval = varargin{ind+1};
-end
-
-% default parameters
-switch Setting.Mode
-    case 'design'
-        Setting.AlgP     = 1;
-        Setting.AlgQ     = 3;
-        Setting.Archive  = '';
-        Setting.IncRate  = -inf;
-        Setting.ProbN    = 50;
-        Setting.ProbFE   = 10000; %test set 50000  train set 5000,3000,10000
-        Setting.InnerFE  = 200;
-        Setting.AlgN     = 5;
-        Setting.AlgFE    = 15000;
-        Setting.AlgRuns  = 5; %test set 30 train set 5
-        if Setting.eval== 1
-            Setting.ProbFE   = 50000; %test set 50000  train set 5000
-            Setting.AlgRuns  = 30; %test set 30 train set 5 % 1 for get convergence curve
-        end
-        Setting.Metric   = 'quality'; % quality/runtimeFE/runtimeSec/auc
-        Setting.Generate = 'learn';   % search/learn
-        Setting.Evaluate = 'exact';   % exact/approximate/intensification/racing
-        Setting.Compare  = 'average'; % average/statistic
-        Setting.Tmax     = [];
-        Setting.Thres    = [];
-        Setting.LSRange  = 0.3;
-        Setting.RacingK  = max(1,round(length(instanceTrain)*0.2));
-        Setting.Surro    = Setting.ProbFE*0.3;
-        Setting          = Input(varargin,Setting,'parameter'); % replace default parameters with user-defined ones
-        Setting          = Input(Setting,'check'); % avoid conflicting parameter settings
-
-        %% construct training problem properties
-        Problem  = struct('name',[],'type',[],'bound',[],'setting',{''},'N',[],'Gmax',[]);
-        seedTrain     = randperm(numel(instanceTrain));
-        seedTest      = randperm(numel(instanceTest))+length(seedTrain);
-        instance      = [instanceTrain,instanceTest];
-
-        for i = 1:numel(instance)
-            Problem(i).name    = prob;
-            Problem(i).setting = '';
-            Problem(i).N       = Setting.ProbN;
-            Problem(i).Gmax    = ceil(Setting.ProbFE/Setting.ProbN)-1;
-            Problem(i).problem_id = Setting.Problem_id;
-        end
-        [Problem,Data,~] = feval(str2func(Problem(1).name),Problem,instance,'construct'); % infill problems' constraints and search boundary, construct data properties
-
-        %% design algorithms
-        Setting = Space(Problem,Setting); % get design space
-end
-
diff --git a/matlab/Main/sigmoid.m b/matlab/Main/sigmoid.m
deleted file mode 100644
index 1098f58..0000000
--- a/matlab/Main/sigmoid.m
+++ /dev/null
@@ -1,6 +0,0 @@
-function [Y] = sigmoid(X)
-%SIGMOID 此处显示有关此函数的摘要
-%   此处显示详细说明
-Y = 1./(1+exp(-X));
-end
-
diff --git a/matlab/Problems/CEC2005 Benchmarks/CEC2005_f1.m b/matlab/Problems/CEC2005 Benchmarks/CEC2005_f1.m
deleted file mode 100644
index 004a063..0000000
--- a/matlab/Problems/CEC2005 Benchmarks/CEC2005_f1.m	
+++ /dev/null
@@ -1,84 +0,0 @@
-function [output1,output2,output3] = CEC2005_f1(varargin)
-% The Shifted Sphere Function from the benchmark for the CEC 2005 Special
-% Session on Real-Parameter Optimization.
-
-%------------------------------Reference-----------------------------------
-% Suganthan P N, Hansen N, Liang J J, et al. Problem definitions and 
-% evaluation criteria for the CEC 2005 special session on real-parameter 
-% optimization[R]. KanGAL report, 2005.
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-switch varargin{end}
-    case 'construct'
-        type     = {'continuous','static','certain'};
-        Problem  = varargin{1};
-        instance = varargin{2};
-        orgData  = load('sphere_func_data');
-        o        = orgData.o;
-        Data     = struct('o',[]);
-        for i = 1:length(instance)
-            Problem(i).type = type;
-            
-            if instance(i) == 1
-                D = 10;
-            elseif instance(i) == 2
-                D = 30;
-            elseif instance(i) == 3
-                D = 50;
-            else
-                error('Only instances 1, 2, and 3 are available.')
-            end           
-            lower = zeros(1,D)-100;
-            upper = zeros(1,D)+100;
-            Problem(i).bound = [lower;upper];
-            
-            if length(o) >= D
-                curr_o = o(1:D);
-            else
-                curr_o = -100+200*rand(1,D);
-            end
-            Data(i).o = curr_o;
-        end
-        output1 = Problem;
-        output2 = Data;
-        
-    case 'repair'
-        Decs = varargin{2};
-        output1 = Decs;
-        
-    case 'evaluate'
-        Data = varargin{1};
-        o    = Data.o;
-        Decs = varargin{2};
-        
-        [N,~] = size(Decs);
-        Decs  = Decs-repmat(o,N,1);
-        
-        fit = sum(Decs.^2,2);
-        output1 = fit-450;
-end
-
-if ~exist('output2','var')
-    output2 = [];
-end
-if ~exist('output3','var')
-    output3 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Problems/CEC2005 Benchmarks/CEC2005_f10.m b/matlab/Problems/CEC2005 Benchmarks/CEC2005_f10.m
deleted file mode 100644
index 7604a79..0000000
--- a/matlab/Problems/CEC2005 Benchmarks/CEC2005_f10.m	
+++ /dev/null
@@ -1,103 +0,0 @@
-function [output1,output2,output3] = CEC2005_f10(varargin)
-% The Shifted Rotated Rastrigin's Function from the benchmark for the CEC
-% 2005 Special Session on Real-Parameter Optimization.
-
-%------------------------------Reference-----------------------------------
-% Suganthan P N, Hansen N, Liang J J, et al. Problem definitions and 
-% evaluation criteria for the CEC 2005 special session on real-parameter 
-% optimization[R]. KanGAL report, 2005.
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-switch varargin{end}
-    case 'construct'
-        type     = {'continuous','static','certain'};
-        Problem  = varargin{1};
-        instance = varargin{2};
-        orgData  = load('rastrigin_func_data');
-        o        = orgData.o;
-        Data     = struct('o',[],'M',[]);
-        for i = 1:length(instance)
-            Problem(i).type = type;
-            
-            if instance(i) == 1
-                D = 10;
-            elseif instance(i) == 2
-                D = 30;
-            elseif instance(i) == 3
-                D = 50;
-            else
-                error('Only instances 1, 2, and 3 are available.')
-            end
-            lower = zeros(1,D)-5;
-            upper = zeros(1,D)+5;
-            Problem(i).bound = [lower;upper];
-            
-            if length(o) >= D
-                curr_o = o(1:D);
-            else
-                curr_o = -5+10*rand(1,D);
-            end
-            Data(i).o = curr_o;
-            
-            if D == 2
-                M = load('rastrigin_M_D2');
-                M = M.M;
-            elseif D == 10
-                M = load('rastrigin_M_D10');
-                M = M.M;
-            elseif D == 30
-                M = load('rastrigin_M_D30');
-                M = M.M;
-            elseif D == 50
-                M = load('rastrigin_M_D50');
-                M = M.M;
-            else
-                M = rot_matrix(D,2);
-            end
-            Data(i).M = M;
-        end
-        output1 = Problem;
-        output2 = Data;
-
-    case 'repair'
-        Decs = varargin{2};
-        output1 = Decs;
-
-    case 'evaluate'
-        Data = varargin{1};
-        o    = Data.o;
-        M    = Data.M;
-        Decs = varargin{2};
-        
-        [N,~] = size(Decs);
-        Decs  = Decs-repmat(o,N,1);
-        Decs  = Decs*M;
-        
-        fit = sum(Decs.^2-10.*cos(2.*pi.*Decs)+10,2);
-        output1 = fit-330;
-end
-
-if ~exist('output2','var')
-    output2 = [];
-end
-if ~exist('output3','var')
-    output3 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Problems/CEC2005 Benchmarks/CEC2005_f11.m b/matlab/Problems/CEC2005 Benchmarks/CEC2005_f11.m
deleted file mode 100644
index d1dbb3b..0000000
--- a/matlab/Problems/CEC2005 Benchmarks/CEC2005_f11.m	
+++ /dev/null
@@ -1,120 +0,0 @@
-function [output1,output2,output3] = CEC2005_f11(varargin)
-% The Shifted Rotated Weierstrass Function from the benchmark for the CEC
-% 2005 Special Session on Real-Parameter Optimization.
-
-%------------------------------Reference-----------------------------------
-% Suganthan P N, Hansen N, Liang J J, et al. Problem definitions and 
-% evaluation criteria for the CEC 2005 special session on real-parameter 
-% optimization[R]. KanGAL report, 2005.
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-switch varargin{end}
-    case 'construct'
-        type     = {'continuous','static','certain'};
-        Problem  = varargin{1};
-        instance = varargin{2};
-        orgData  = load('weierstrass_data');
-        o        = orgData.o;
-        Data     = struct('o',[],'M',[],'c1',[],'c2',[],'c',[]);
-        for i = 1:length(instance)
-            Problem(i).type = type;
-
-            if instance(i) == 1
-                D = 10;
-            elseif instance(i) == 2
-                D = 30;
-            elseif instance(i) == 3
-                D = 50;
-            else
-                error('Only instances 1, 2, and 3 are available.')
-            end
-            lower = zeros(1,D)-0.5;
-            upper = zeros(1,D)+0.5;
-            Problem(i).bound = [lower;upper];
-            
-            if length(o) >= D
-                curr_o = o(1:D);
-            else
-                curr_o = -0.5+0.5*rand(1,D);
-            end
-            Data(i).o = curr_o;
-            
-            if D == 2
-                M = load('weierstrass_M_D2');
-                M = M.M;
-            elseif D == 10
-                M = load('weierstrass_M_D10');
-                M = M.M;
-            elseif D == 30
-                M = load('weierstrass_M_D30');
-                M = M.M;
-            elseif D == 50
-                M = load('weierstrass_M_D50');
-                M = M.M;
-            else
-                M = rot_matrix(D,5);
-            end
-            Data(i).M = M;
-            
-            a    = 0.5;
-            b    = 3;
-            kmax = 20;
-            c1   = a.^(0:kmax);
-            c2   = 2*pi*b.^(0:kmax);
-            c    = -w(0.5,c1,c2);
-            Data(i).c1 = c1;
-            Data(i).c2 = c2;
-            Data(i).c  = c;
-        end
-        output1 = Problem;
-        output2 = Data;
-
-    case 'repair'
-        Decs = varargin{2};
-        output1 = Decs;
-
-    case 'evaluate'
-        Data = varargin{1};
-        o    = Data.o;
-        M    = Data.M;
-        c1   = Data.c1;
-        c2   = Data.c2;
-        c    = Data.c;
-        Decs = varargin{2};
-        
-        [N,D] = size(Decs);
-        Decs  = Decs-repmat(o,N,1);
-        Decs  = Decs*M+0.5;
-        
-        fit = 0;
-        for i = 1:D
-            fit = fit+w(Decs(:,i)',c1,c2);
-        end
-        fit = fit+repmat(c*D,N,1);
-        output1 = fit+90;
-end
-
-if ~exist('output2','var')
-    output2 = [];
-end
-if ~exist('output3','var')
-    output3 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Problems/CEC2005 Benchmarks/CEC2005_f12.m b/matlab/Problems/CEC2005 Benchmarks/CEC2005_f12.m
deleted file mode 100644
index 946efac..0000000
--- a/matlab/Problems/CEC2005 Benchmarks/CEC2005_f12.m	
+++ /dev/null
@@ -1,99 +0,0 @@
-function [output1,output2,output3] = CEC2005_f12(varargin)
-% The Schwefel's Problem 2.13 from the benchmark for the CEC 2005 Special
-% Session on Real-Parameter Optimization.
-
-%------------------------------Reference-----------------------------------
-% Suganthan P N, Hansen N, Liang J J, et al. Problem definitions and 
-% evaluation criteria for the CEC 2005 special session on real-parameter 
-% optimization[R]. KanGAL report, 2005.
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-switch varargin{end}
-    case 'construct'
-        type     = {'continuous','static','certain'};
-        Problem  = varargin{1};
-        instance = varargin{2};
-        orgData  = load('schwefel_213_data');
-        a        = orgData.a;
-        b        = orgData.b;
-        alpha    = orgData.alpha;
-        Data     = struct('a',[],'b',[],'A',[]);
-        for i = 1:length(instance)
-            Problem(i).type = type;
-            
-            if instance(i) == 1
-                D = 10;
-            elseif instance(i) == 2
-                D = 30;
-            elseif instance(i) == 3
-                D = 50;
-            else
-                error('Only instances 1, 2, and 3 are available.')
-            end
-            lower = zeros(1,D)-100;
-            upper = zeros(1,D)+100;
-            Problem(i).bound = [lower;upper];
-            
-            if length(alpha) >= D
-                curr_alpha = alpha(1:D);
-                curr_a = a(1:D,1:D);
-                curr_b = b(1:D,1:D);
-            else
-                curr_alpha = -3+6*rand(1,D);
-                curr_a = round(-100+200.*rand(D,D));
-                curr_b = round(-100+200.*rand(D,D));
-            end
-            curr_alpha = repmat(curr_alpha,D,1);
-            curr_A = sum(curr_a.*sin(curr_alpha)+curr_b.*cos(curr_alpha),2);
-            Data(i).a = curr_a;
-            Data(i).b = curr_b;
-            Data(i).A = curr_A;
-        end
-        output1 = Problem;
-        output2 = Data;
-
-    case 'repair'
-        Decs = varargin{2};
-        output1 = Decs;
-
-    case 'evaluate'
-        Data = varargin{1};
-        a    = Data.a;
-        b    = Data.b;
-        A    = Data.A;
-        Decs = varargin{2};
-        
-        [N,D] = size(Decs);
-        fit = zeros(N,1);
-        for i = 1:N
-            xx = repmat(Decs(i,:),D,1);
-            B  = sum(a.*sin(xx)+b.*cos(xx),2);
-            fit(i) = sum((A-B).^2,1);
-        end
-        output1 = fit-460;
-end
-
-if ~exist('output2','var')
-    output2 = [];
-end
-if ~exist('output3','var')
-    output3 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Problems/CEC2005 Benchmarks/CEC2005_f2.m b/matlab/Problems/CEC2005 Benchmarks/CEC2005_f2.m
deleted file mode 100644
index 92c2aa4..0000000
--- a/matlab/Problems/CEC2005 Benchmarks/CEC2005_f2.m	
+++ /dev/null
@@ -1,87 +0,0 @@
-function [output1,output2,output3] = CEC2005_f2(varargin)
-% The Shifted Schwefel's Problem from the benchmark for the CEC 2005
-% Special Session on Real-Parameter Optimization.
-
-%------------------------------Reference-----------------------------------
-% Suganthan P N, Hansen N, Liang J J, et al. Problem definitions and 
-% evaluation criteria for the CEC 2005 special session on real-parameter 
-% optimization[R]. KanGAL report, 2005.
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-switch varargin{end}
-    case 'construct'
-        type     = {'continuous','static','certain'};
-        Problem  = varargin{1};
-        instance = varargin{2};
-        orgData  = load('schwefel_102_data');
-        o        = orgData.o;
-        Data     = struct('o',[]);
-        for i = 1:length(instance)
-            Problem(i).type = type;
-            
-            if instance(i) == 1
-                D = 10;
-            elseif instance(i) == 2
-                D = 30;
-            elseif instance(i) == 3
-                D = 50;
-            else
-                error('Only instances 1, 2, and 3 are available.')
-            end
-            lower = zeros(1,D)-100;
-            upper = zeros(1,D)+100;
-            Problem(i).bound = [lower;upper];
-            
-            if length(o) >= D
-                curr_o = o(1:D);
-            else
-                curr_o = -100+200*rand(1,D);
-            end
-            Data(i).o = curr_o;
-        end     
-        output1 = Problem;
-        output2 = Data;
-        
-    case 'repair'
-        Decs = varargin{2};
-        output1 = Decs;
-
-    case 'evaluate'
-        Data = varargin{1};
-        o    = Data.o;
-        Decs = varargin{2};
-        
-        [N,D] = size(Decs);
-        Decs  = Decs-repmat(o,N,1);
-        
-        fit = 0;
-        for i = 1:D
-            fit = fit+sum(Decs(:,1:i),2).^2;
-        end
-        output1 = fit-450;
-end
-
-if ~exist('output2','var')
-    output2 = [];
-end
-if ~exist('output3','var')
-    output3 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Problems/CEC2005 Benchmarks/CEC2005_f3.m b/matlab/Problems/CEC2005 Benchmarks/CEC2005_f3.m
deleted file mode 100644
index 8419ae2..0000000
--- a/matlab/Problems/CEC2005 Benchmarks/CEC2005_f3.m	
+++ /dev/null
@@ -1,108 +0,0 @@
-function [output1,output2,output3] = CEC2005_f3(varargin)
-% The Shifted Rotated High Conditioned Elliptic Function 1.2 from the
-% benchmark for the CEC 2005 Special Session on Real-Parameter Optimization.
-
-%------------------------------Reference-----------------------------------
-% Suganthan P N, Hansen N, Liang J J, et al. Problem definitions and 
-% evaluation criteria for the CEC 2005 special session on real-parameter 
-% optimization[R]. KanGAL report, 2005.
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-switch varargin{end}
-    case 'construct'
-        type     = {'continuous','static','certain'};
-        Problem  = varargin{1};
-        instance = varargin{2};
-        orgData  = load('high_cond_elliptic_rot_data');
-        o        = orgData.o;
-        Data     = struct('o',[],'M',[]);
-        for i = 1:length(instance)
-             Problem(i).type = type;
-
-            if instance(i) == 1
-                D = 10;
-            elseif instance(i) == 2
-                D = 30;
-            elseif instance(i) == 3
-                D = 50;
-            else
-                error('Only instances 1, 2, and 3 are available.')
-            end
-            lower = zeros(1,D)-100;
-            upper = zeros(1,D)+100;
-            Problem(i).bound = [lower;upper];
-            
-            if length(o) >= D
-                curr_o = o(1:D);
-            else
-                curr_o = -100+200*rand(1,D);
-            end
-            Data(i).o = curr_o;
-            
-            if D == 2
-                M = load('elliptic_M_D2');
-                M = M.M;
-            elseif D == 10
-                M = load('elliptic_M_D10');
-                M = M.M;
-            elseif D == 30
-                M = load('elliptic_M_D30');
-                M = M.M;
-            elseif D == 50
-                M = load('elliptic_M_D50');
-                M = M.M;
-            else
-                A = normrnd(0,1,D,D);
-                [M,~] = cGram_Schmidt(A);
-            end
-            Data(i).M = M;
-        end      
-        output1 = Problem;
-        output2 = Data;
-        
-   case 'repair'
-        Decs = varargin{2};
-        output1 = Decs;
-    
-    case 'evaluate'
-        Data = varargin{1};
-        o    = Data.o;
-        M    = Data.M;
-        Decs = varargin{2};
-        
-        [N,D] = size(Decs);
-        Decs  = Decs-repmat(o,N,1);
-        Decs  = Decs*M;
-        
-        a = 1e+6;
-        fit = 0;
-        for i = 1:D
-            fit = fit+a.^((i-1)/(D-1)).*Decs(:,i).^2;
-        end
-        output1= fit-450;
-end
-
-if ~exist('output2','var')
-    output2 = [];
-end
-if ~exist('output3','var')
-    output3 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Problems/CEC2005 Benchmarks/CEC2005_f4.m b/matlab/Problems/CEC2005 Benchmarks/CEC2005_f4.m
deleted file mode 100644
index 7324fe3..0000000
--- a/matlab/Problems/CEC2005 Benchmarks/CEC2005_f4.m	
+++ /dev/null
@@ -1,89 +0,0 @@
-function [output1, output2, output3] = CEC2005_f4(varargin)
-% The Shifted Schwefel's Problem 1.2 with Noise in Fitness from the
-% benchmark for the CEC 2005 Special Session on Real-Parameter Optimization.
-
-%------------------------------Reference-----------------------------------
-% Suganthan P N, Hansen N, Liang J J, et al. Problem definitions and 
-% evaluation criteria for the CEC 2005 special session on real-parameter 
-% optimization[R]. KanGAL report, 2005.
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-switch varargin{end}
-    case 'construct'
-        type     = {'continuous','static','certain'};
-        Problem  = varargin{1};
-        instance = varargin{2};
-        orgData  = load('schwefel_102_data');
-        o        = orgData.o;
-        Data     = struct('o',[]);
-        for i = 1:length(instance)
-            Problem(i).type = type;
-            
-            if instance(i) == 1
-                D = 10;
-            elseif instance(i) == 2
-                D = 30;
-            elseif instance(i) == 3
-                D = 50;
-            else
-                error('Only instances 1, 2, and 3 are available.')
-            end
-            lower   = zeros(1,D)-100;
-            upper   = zeros(1,D)+100;
-            Problem(i).bound = [lower;upper];
-            
-            
-            if length(o) >= D
-                curr_o = o(1:D);
-            else
-                curr_o = -100+200*rand(1,D);
-            end
-            Data(i).o = curr_o;
-        end
-        output1 = Problem;
-        output2 = Data;
-        
-    case 'repair'
-        Decs = varargin{2};
-        output1 = Decs;
-    
-    case 'evaluate'
-        Data = varargin{1};
-        o    = Data.o;
-        Decs = varargin{2};
-        
-        [N,D] = size(Decs);
-        Decs  = Decs-repmat(o,N,1);
-        
-        fit = 0;
-        for i = 1:D
-            fit = fit+sum(Decs(:,1:i),2).^2;
-        end
-        fit = fit.*(1+0.4.*abs(normrnd(0,1,N,1)));
-        output1 = fit-450;
-end
-
-if ~exist('output2','var')
-    output2 = [];
-end
-if ~exist('output3','var')
-    output3 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Problems/CEC2005 Benchmarks/CEC2005_f5.m b/matlab/Problems/CEC2005 Benchmarks/CEC2005_f5.m
deleted file mode 100644
index cca5e02..0000000
--- a/matlab/Problems/CEC2005 Benchmarks/CEC2005_f5.m	
+++ /dev/null
@@ -1,96 +0,0 @@
-function [output1, output2, output3] = CEC2005_f5(varargin)
-% The Schwefel's Problem 2.6 with Global Optimum on Bounds from the
-% benchmark for the CEC 2005 Special Session on Real-Parameter Optimization.
-
-%------------------------------Reference-----------------------------------
-% Suganthan P N, Hansen N, Liang J J, et al. Problem definitions and 
-% evaluation criteria for the CEC 2005 special session on real-parameter 
-% optimization[R]. KanGAL report, 2005.
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-switch varargin{end}
-    case 'construct'
-        type     = {'continuous','static','certain'};
-        Problem  = varargin{1};
-        instance = varargin{2};
-        orgData  = load('schwefel_206_data');
-        o        = orgData.o;
-        A        = orgData.A;
-        Data     = struct('A',[],'B',[]);
-        for i = 1:length(instance)
-            Problem(i).type = type;
-
-            if instance(i) == 1
-                D = 10;
-            elseif instance(i) == 2
-                D = 30;
-            elseif instance(i) == 3
-                D = 50;
-            else
-                error('Only instances 1, 2, and 3 are available.')
-            end
-            lower = zeros(1,D)-100;
-            upper = zeros(1,D)+100;
-            Problem(i).bound = [lower;upper];
-            
-            if length(o) >= D
-                curr_o = o(1:D);
-                curr_A = A(1:D,1:D);
-            else
-                curr_o = -100+200*rand(1,D);
-                curr_A = round(-100+2*100.*rand(D,D));
-                while det(curr_A) == 0
-                    curr_A = round(-100+2*100.*rand(D,D));
-                end
-            end
-            curr_o(1:ceil(D/4)) = -100;
-            curr_o(max(floor(0.75*D),1):D) = 100;
-            curr_B = curr_A*curr_o';
-            Data(i).A = curr_A;
-            Data(i).B = curr_B;
-        end
-        output1 = Problem;
-        output2 = Data;
-        
-    case 'repair'
-        Decs = varargin{2};
-        output1 = Decs;
-    
-    case 'evaluate'
-        Data  = varargin{1};
-        A     = Data.A;
-        B     = Data.B;
-        Decs  = varargin{2};
-        [N,~] = size(Decs);
-        
-        fit = zeros(N,1);
-        for i = 1:N
-            fit(i) = max(abs(A*(Decs(i,:)')-B));
-        end
-        output1 = fit-310;
-end
-
-if ~exist('output2','var')
-    output2 = [];
-end
-if ~exist('output3','var')
-    output3 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Problems/CEC2005 Benchmarks/CEC2005_f6.m b/matlab/Problems/CEC2005 Benchmarks/CEC2005_f6.m
deleted file mode 100644
index 925a71b..0000000
--- a/matlab/Problems/CEC2005 Benchmarks/CEC2005_f6.m	
+++ /dev/null
@@ -1,84 +0,0 @@
-function [output1, output2, output3] = CEC2005_f6(varargin)
-% The Shifted Rosenbrock's Function from the benchmark for the CEC 2005
-% Special Session on Real-Parameter Optimization.
-
-%------------------------------Reference-----------------------------------
-% Suganthan P N, Hansen N, Liang J J, et al. Problem definitions and 
-% evaluation criteria for the CEC 2005 special session on real-parameter 
-% optimization[R]. KanGAL report, 2005.
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-switch varargin{end}
-    case 'construct'
-        type     = {'continuous','static','certain'};
-        Problem  = varargin{1};
-        instance = varargin{2};
-        orgData  = load('rosenbrock_func_data');
-        o        = orgData.o;
-        Data     = struct('o',[]);
-        for i = 1:length(instance)
-            Problem(i).type = type;
-            
-            if instance(i) == 1
-                D = 10;
-            elseif instance(i) == 2
-                D = 30;
-            elseif instance(i) == 3
-                D = 50;
-            else
-                error('Only instances 1, 2, and 3 are available.')
-            end
-            lower = zeros(1,D) - 100;
-            upper = zeros(1,D) + 100;
-            Problem(i).bound = [lower;upper];        
-            
-            if length(o) >= D
-                curr_o = o(1:D);
-            else
-                curr_o = -90+180*rand(1,D);
-            end
-            Data(i).o = curr_o;
-        end
-        output1 = Problem;
-        output2 = Data;
-        
-   case 'repair'
-        Decs = varargin{2};
-        output1 = Decs; 
-    
-    case 'evaluate'
-        Data  = varargin{1};
-        o     = Data.o;
-        Decs  = varargin{2};
-        
-        [N,D] = size(Decs);
-        Decs  = Decs-repmat(o,N,1)+1;
-        
-        fit = sum(100.*(Decs(:,1:D-1).^2-Decs(:,2:D)).^2+(Decs(:,1:D-1)-1).^2,2);
-        output1 = fit+390;
-end
-
-if ~exist('output2','var')
-    output2 = [];
-end
-if ~exist('output3','var')
-    output3 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Problems/CEC2005 Benchmarks/CEC2005_f7.m b/matlab/Problems/CEC2005 Benchmarks/CEC2005_f7.m
deleted file mode 100644
index 22d77cb..0000000
--- a/matlab/Problems/CEC2005 Benchmarks/CEC2005_f7.m	
+++ /dev/null
@@ -1,108 +0,0 @@
-function [output1,output2,output3] = CEC2005_f7(varargin)
-% The Shifted Rotated Griewank's Function without Bounds from the benchmark
-% for the CEC 2005 Special Session on Real-Parameter Optimization.
-
-%------------------------------Reference-----------------------------------
-% Suganthan P N, Hansen N, Liang J J, et al. Problem definitions and 
-% evaluation criteria for the CEC 2005 special session on real-parameter 
-% optimization[R]. KanGAL report, 2005.
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-switch varargin{end}
-    case 'construct'
-        type     = {'continuous','static','certain'};
-        Problem  = varargin{1};
-        instance = varargin{2};
-        orgData  = load('griewank_func_data');
-        o        = orgData.o;
-        Data     = struct('o',[],'M',[]);
-        for i = 1:length(instance)
-            Problem(i).type = type;
-
-            if instance(i) == 1
-                D = 10;
-            elseif instance(i) == 2
-                D = 30;
-            elseif instance(i) == 3
-                D = 50;
-            else
-                error('Only instances 1, 2, and 3 are available.')
-            end
-            lower = zeros(1,D) - 600;
-            upper = zeros(1,D) + 600;
-            Problem(i).bound = [lower;upper];
-            
-            if length(o) >= D
-                curr_o = o(1:D);
-            else
-                curr_o = -600+0*rand(1,D);
-            end
-            Data(i).o = curr_o;
-            
-            if D == 2
-                M = load('griewank_M_D2');
-                M = M.M;
-            elseif D == 10
-                M = load('griewank_M_D10');
-                M = M.M;
-            elseif D == 30
-                M = load('griewank_M_D30');
-                M = M.M;
-            elseif D == 50
-                M = load('griewank_M_D50');
-                M = M.M;
-            else
-                M = rot_matrix(D,3);
-                M = M.*(1+0.3.*normrnd(0,1,D,D));
-            end
-            Data(i).M = M;
-        end      
-        output1 = Problem;
-        output2 = Data;
-        
-    case 'repair'
-        Decs = varargin{2};
-        output1 = Decs;
-    
-    case 'evaluate'
-        Data  = varargin{1};
-        o     = Data.o;
-        M     = Data.M;
-        Decs  = varargin{2};
-        
-        [N,D] = size(Decs);
-        Decs  = Decs-repmat(o,N,1);
-        Decs  = Decs*M;
-        
-        fit = 1;
-        for i = 1:D
-            fit = fit.*cos(Decs(:,i)./sqrt(i));
-        end
-        fit = sum(Decs.^2,2)./4000-fit+1;
-        output1= fit-180;
-end
-
-if ~exist('output2','var')
-    output2 = [];
-end
-if ~exist('output3','var')
-    output3 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Problems/CEC2005 Benchmarks/CEC2005_f8.m b/matlab/Problems/CEC2005 Benchmarks/CEC2005_f8.m
deleted file mode 100644
index 2dc8d6a..0000000
--- a/matlab/Problems/CEC2005 Benchmarks/CEC2005_f8.m	
+++ /dev/null
@@ -1,106 +0,0 @@
-function [output1,output2,output3] = CEC2005_f8(varargin)
-% The Shifted Rotated Ackley's Function with Global Optimum on Bounds from
-% the benchmark for the CEC 2005 Special Session on Real-Parameter
-% Optimization.
-
-%------------------------------Reference-----------------------------------
-% Suganthan P N, Hansen N, Liang J J, et al. Problem definitions and 
-% evaluation criteria for the CEC 2005 special session on real-parameter 
-% optimization[R]. KanGAL report, 2005.
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-switch varargin{end}
-    case 'construct'
-        type     = {'continuous','static','certain'};
-        Problem  = varargin{1};
-        instance = varargin{2};
-        orgData  = load('ackley_func_data');
-        o        = orgData.o;
-        Data     = struct('o',[],'M',[]);
-        for i = 1:length(instance)
-            Problem(i).type = type;
-            
-            if instance(i) == 1
-                D = 10;
-            elseif instance(i) == 2
-                D = 30;
-            elseif instance(i) == 3
-                D = 50;
-            else
-                error('Only instances 1, 2, and 3 are available.')
-            end
-            lower = zeros(1,D) - 32;
-            upper = zeros(1,D) + 32;
-            Problem(i).bound = [lower;upper];
-            
-            if length(o) >= D
-                curr_o = o(1:D);
-            else
-                curr_o = -30+60*rand(1,D);
-            end
-            curr_o(2.*(1:floor(D/2))-1) = -32;
-            Data(i).o = curr_o;
-            
-            if D == 2
-                M = load('ackley_M_D2');
-                M = M.M;
-            elseif D == 10
-                M = load('ackley_M_D10');
-                M = M.M;
-            elseif D == 30
-                M = load('ackley_M_D30');
-                M = M.M;
-            elseif D == 50
-                M = load('ackley_M_D50');
-                M = M.M;
-            else
-                M = rot_matrix(D,100);
-            end
-            Data(i).M = M;
-        end
-        output1 = Problem;
-        output2 = Data;
-        
-    case 'repair'
-        Decs = varargin{2};
-        output1 = Decs;
-    
-    case 'evaluate'
-        Data = varargin{1};
-        o    = Data.o;
-        M    = Data.M;
-        Decs = varargin{2};
-        
-        [N,D] = size(Decs);
-        Decs  = Decs-repmat(o,N,1);
-        Decs  = Decs*M;
-        
-        fit = sum(Decs.^2,2);
-        fit = 20-20.*exp(-0.2.*sqrt(fit./D))-exp(sum(cos(2.*pi.*Decs),2)./D)+exp(1);
-        output1 = fit-140;
-end
-
-if ~exist('output2','var')
-    output2 = [];
-end
-if ~exist('output3','var')
-    output3 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Problems/CEC2005 Benchmarks/CEC2005_f9.m b/matlab/Problems/CEC2005 Benchmarks/CEC2005_f9.m
deleted file mode 100644
index 52d0e16..0000000
--- a/matlab/Problems/CEC2005 Benchmarks/CEC2005_f9.m	
+++ /dev/null
@@ -1,84 +0,0 @@
-function [output1,output2,output3] = CEC2005_f9(varargin)
-% The Shifted Rastrigin's Function from the benchmark for the CEC 2005
-% Special Session on Real-Parameter Optimization.
-
-%------------------------------Reference-----------------------------------
-% Suganthan P N, Hansen N, Liang J J, et al. Problem definitions and 
-% evaluation criteria for the CEC 2005 special session on real-parameter 
-% optimization[R]. KanGAL report, 2005.
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-switch varargin{end}
-    case 'construct'
-        type     = {'continuous','static','certain'};
-        Problem  = varargin{1};
-        instance = varargin{2};
-        orgData  = load('rastrigin_func_data');
-        o        = orgData.o;
-        Data     = struct('o',[]);
-        for i = 1:length(instance)
-            Problem(i).type = type;
-            
-            if instance(i) == 1
-                D = 10;
-            elseif instance(i) == 2
-                D = 30;
-            elseif instance(i) == 3
-                D = 50;
-            else
-                error('Only instances 1, 2, and 3 are available.')
-            end
-            lower = zeros(1,D)-5;
-            upper = zeros(1,D)+5;
-            Problem(i).bound = [lower;upper];
-            
-            if length(o) >= D
-                curr_o = o(1:D);
-            else
-                curr_o = -5+10*rand(1,D);
-            end
-            Data(i).o = curr_o;
-        end
-        output1 = Problem;
-        output2 = Data;
-        
-   case 'repair'
-        Decs = varargin{2};
-        output1 = Decs;
-    
-    case 'evaluate'
-        Data = varargin{1};
-        o    = Data.o;
-        Decs = varargin{2};
-        ps = size(Decs,1);
-        
-        Decs = Decs-repmat(o,ps,1);
-        
-        fit = sum(Decs.^2-10.*cos(2.*pi.*Decs)+10,2);
-        output1 = fit-330;
-end
-
-if ~exist('output2','var')
-    output2 = [];
-end
-if ~exist('output3','var')
-    output3 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Problems/CEC2005 Benchmarks/Data/EF8F2_func_data.mat b/matlab/Problems/CEC2005 Benchmarks/Data/EF8F2_func_data.mat
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diff --git a/matlab/Problems/CEC2005 Benchmarks/Data/ackley_M_D10.mat b/matlab/Problems/CEC2005 Benchmarks/Data/ackley_M_D10.mat
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index 473d707..0000000
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index 4d5f6a9..0000000
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diff --git a/matlab/Problems/CEC2005 Benchmarks/Data/ackley_func_data.mat b/matlab/Problems/CEC2005 Benchmarks/Data/ackley_func_data.mat
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diff --git a/matlab/Problems/CEC2005 Benchmarks/Data/cGram_Schmidt.m b/matlab/Problems/CEC2005 Benchmarks/Data/cGram_Schmidt.m
deleted file mode 100644
index f7e9db0..0000000
--- a/matlab/Problems/CEC2005 Benchmarks/Data/cGram_Schmidt.m	
+++ /dev/null
@@ -1,16 +0,0 @@
- function [q,r] = cGram_Schmidt(A)
-% Computes the QR factorization of $A$ via classical Gram Schmid 
-
-[~,m] = size(A);
-q = A;
-for j = 1:m
-    for i = 1:j-1
-        r(i,j) = q(:,j)'*q(:,i);
-    end
-    for i = 1:j-1
-        q(:,j) = q(:,j)-r(i,j)*q(:,i);
-    end
-    t = norm(q(:,j),2);
-    q(:,j) = q(:,j)/t;
-    r(j,j) = t;
-end
\ No newline at end of file
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diff --git a/matlab/Problems/CEC2005 Benchmarks/Data/rastrigin_func_data.mat b/matlab/Problems/CEC2005 Benchmarks/Data/rastrigin_func_data.mat
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index 583fc0a..0000000
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diff --git a/matlab/Problems/CEC2005 Benchmarks/Data/rosenbrock_func_data.mat b/matlab/Problems/CEC2005 Benchmarks/Data/rosenbrock_func_data.mat
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diff --git a/matlab/Problems/CEC2005 Benchmarks/Data/rot_matrix.m b/matlab/Problems/CEC2005 Benchmarks/Data/rot_matrix.m
deleted file mode 100644
index 2a1e416..0000000
--- a/matlab/Problems/CEC2005 Benchmarks/Data/rot_matrix.m	
+++ /dev/null
@@ -1,10 +0,0 @@
-function M = rot_matrix(D,c)
-A = normrnd(0,1,D,D);
-P = cGram_Schmidt(A);
-A = normrnd(0,1,D,D);
-Q = cGram_Schmidt(A);
-u = rand(1,D);
-D = c.^((u-min(u))./(max(u)-min(u)));
-D = diag(D);
-M = P*D*Q;
-end
\ No newline at end of file
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--- a/matlab/Problems/CEC2005 Benchmarks/Data/w.m	
+++ /dev/null
@@ -1,5 +0,0 @@
-function y = w(x,c1,c2)
-y = zeros(length(x),1);
-for k = 1:length(x)
-	y(k) = sum(c1 .* cos(c2.*x(:,k)));
-end
\ No newline at end of file
diff --git a/matlab/Problems/CEC2005 Benchmarks/Data/weierstrass_M_D10.mat b/matlab/Problems/CEC2005 Benchmarks/Data/weierstrass_M_D10.mat
deleted file mode 100644
index e69c0e3..0000000
Binary files a/matlab/Problems/CEC2005 Benchmarks/Data/weierstrass_M_D10.mat and /dev/null differ
diff --git a/matlab/Problems/CEC2005 Benchmarks/Data/weierstrass_M_D2.mat b/matlab/Problems/CEC2005 Benchmarks/Data/weierstrass_M_D2.mat
deleted file mode 100644
index 1eea8a8..0000000
Binary files a/matlab/Problems/CEC2005 Benchmarks/Data/weierstrass_M_D2.mat and /dev/null differ
diff --git a/matlab/Problems/CEC2005 Benchmarks/Data/weierstrass_M_D30.mat b/matlab/Problems/CEC2005 Benchmarks/Data/weierstrass_M_D30.mat
deleted file mode 100644
index 0b9e129..0000000
Binary files a/matlab/Problems/CEC2005 Benchmarks/Data/weierstrass_M_D30.mat and /dev/null differ
diff --git a/matlab/Problems/CEC2005 Benchmarks/Data/weierstrass_M_D50.mat b/matlab/Problems/CEC2005 Benchmarks/Data/weierstrass_M_D50.mat
deleted file mode 100644
index f57bfff..0000000
Binary files a/matlab/Problems/CEC2005 Benchmarks/Data/weierstrass_M_D50.mat and /dev/null differ
diff --git a/matlab/Problems/CEC2005 Benchmarks/Data/weierstrass_data.mat b/matlab/Problems/CEC2005 Benchmarks/Data/weierstrass_data.mat
deleted file mode 100644
index 75e36e9..0000000
Binary files a/matlab/Problems/CEC2005 Benchmarks/Data/weierstrass_data.mat and /dev/null differ
diff --git a/matlab/Problems/Real-World/BBOB/bbob.m b/matlab/Problems/Real-World/BBOB/bbob.m
deleted file mode 100644
index 867135a..0000000
--- a/matlab/Problems/Real-World/BBOB/bbob.m
+++ /dev/null
@@ -1,79 +0,0 @@
-function [output1,output2,output3] = bbob(varargin)
-% The beanforming problem in RIS-aided communications.
-
-%------------------------------Reference-----------------------------------
-% Yan B, Zhao Q, Li M, et al. Fitness landscape analysis and niching 
-% genetic approach for hybrid beamforming in RIS-aided communications[J]. 
-% Applied Soft Computing, 2022, 131: 109725.
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-switch varargin{end}
-    case 'construct' % define problem properties
-        Problem  = varargin{1};
-        instance = varargin{2};       
-        Data     = struct('o',[]);
-        for i = 1:length(instance)
-            if instance(i) == 1
-                D = 2;
-            elseif instance(i) == 2
-                D = 5;
-            elseif instance(i) == 3
-                D = 10;
-            elseif instance(i) == 4
-                D = 20;
-            elseif instance(i) == 5
-                D = 90;
-            else
-                error('Only instances 1, 2, and 3 are available.')
-            end  
-            
-            lower = zeros(1,D)-5;
-            upper = zeros(1,D)+5;
-            
-            Problem(i).type = {'continuous','static','certain'};
-            Problem(i).bound = [lower;upper];
-            %Problem(i).problem_index = 1
-            Data(i).o = Data; % do nothing but add data(i)
-        end
-        
-        output1 = Problem;
-        output2 = Data;
-
-    case 'repair' % repair solutions
-        Decs = varargin{2};
-        output1 = Decs;
-    
-    case 'evaluate' % evaluate solution's fitness
-        problem_id  = varargin{3};
-        Decs = varargin{2};
-        [q,dim] = size(Decs);
-        
-        res = py.IOH_Test.BBOB(Decs,dim,problem_id);
-        res = double(res).';
-        output1 = res; % matrix for saving objective function values
-end
-
-if ~exist('output2','var')
-    output2 = [];
-end
-if ~exist('output3','var')
-    output3 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Problems/Real-World/Beanforming/Beanforming.mat b/matlab/Problems/Real-World/Beanforming/Beanforming.mat
deleted file mode 100644
index 2170f3f..0000000
Binary files a/matlab/Problems/Real-World/Beanforming/Beanforming.mat and /dev/null differ
diff --git a/matlab/Problems/Real-World/Beanforming/ULA_fun.m b/matlab/Problems/Real-World/Beanforming/ULA_fun.m
deleted file mode 100644
index 69c3734..0000000
--- a/matlab/Problems/Real-World/Beanforming/ULA_fun.m
+++ /dev/null
@@ -1,4 +0,0 @@
-function [ h ] = ULA_fun( phi ,N)
-    h=exp(1j*pi*sin(phi).*(0:N-1)');
-end
-
diff --git a/matlab/Problems/Real-World/Beanforming/beamforming.m b/matlab/Problems/Real-World/Beanforming/beamforming.m
deleted file mode 100644
index 30c0137..0000000
--- a/matlab/Problems/Real-World/Beanforming/beamforming.m
+++ /dev/null
@@ -1,73 +0,0 @@
-function [output1,output2,output3] = beamforming(varargin)
-% The beanforming problem in RIS-aided communications.
-
-%------------------------------Reference-----------------------------------
-% Yan B, Zhao Q, Li M, et al. Fitness landscape analysis and niching 
-% genetic approach for hybrid beamforming in RIS-aided communications[J]. 
-% Applied Soft Computing, 2022, 131: 109725.
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-switch varargin{end}
-    case 'construct' % define problem properties
-        Problem  = varargin{1};
-        instance = varargin{2};       
-
-        orgData  = load('Beanforming.mat','Data');
-        Data = orgData.Data((instance));
-        for i = 1:length(instance)
-            D = size(Data(i).G,1);
-            phases_cnt = 2^Data(i).b-1;
-            lower = zeros(1,D); % 1*D, lower bound of the D-dimension decision space
-            upper = repmat(phases_cnt,1,D); % 1*D, upper bound of the D-dimension decision space
-            Problem(i).type = {'discrete','static','certain'};
-            Problem(i).bound = [lower;upper];
-        end
-        
-        output1 = Problem;
-        output2 = Data;
-
-    case 'repair' % repair solutions
-        Decs = varargin{2};
-        output1 = Decs;
-    
-    case 'evaluate' % evaluate solution's fitness
-        Data = varargin{1}; % load problem data
-        m    = varargin{2}; % load the current solution(s)
-        
-        b     = Data.b;
-        PT    = Data.PT;
-        G     = Data.G;
-        Hd    = Data.Hd;
-        Hr    = Data.Hr;
-        omega = Data.omega;
-      
-        sR = get_sum_rate(m,b,Hd, Hr,G,PT,omega); % calculate objective value
-        sR = sR'; % N*1
-
-        output1 = sR; % matrix for saving objective function values
-end
-
-if ~exist('output2','var')
-    output2 = [];
-end
-if ~exist('output3','var')
-    output3 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Problems/Real-World/Beanforming/channel_G.m b/matlab/Problems/Real-World/Beanforming/channel_G.m
deleted file mode 100644
index b006ab1..0000000
--- a/matlab/Problems/Real-World/Beanforming/channel_G.m
+++ /dev/null
@@ -1,4 +0,0 @@
-function G=channel_G(AP_angle,IRS_angle,G_sig,eb1,eb2,N,M)
-    G=eb1.*ULA_fun( IRS_angle ,N)*(ULA_fun( AP_angle ,M))'+eb2.*G_sig;
-end
-
diff --git a/matlab/Problems/Real-World/Beanforming/channel_Hr.m b/matlab/Problems/Real-World/Beanforming/channel_Hr.m
deleted file mode 100644
index a772bde..0000000
--- a/matlab/Problems/Real-World/Beanforming/channel_Hr.m
+++ /dev/null
@@ -1,10 +0,0 @@
-function Hr=channel_Hr(User_angle,Hr_sig,eb1,eb2,K,N)
-    Hr=zeros(K,N);
-    for k0=1:K
-        User_angle_k=User_angle(k0);
-        hr_sig=Hr_sig(k0,:);
-        hr=eb1.*(ULA_fun( User_angle_k ,N))'+eb2.*hr_sig;
-        Hr(k0,:)=hr;
-    end
-end
-
diff --git a/matlab/Problems/Real-World/Beanforming/generateInstance.m b/matlab/Problems/Real-World/Beanforming/generateInstance.m
deleted file mode 100644
index 74d363b..0000000
--- a/matlab/Problems/Real-World/Beanforming/generateInstance.m
+++ /dev/null
@@ -1,19 +0,0 @@
-function generateInstance
-K    = 4;
-Nt   = 4;
-NR2  = 40:40:400;
-b    = 2;
-SNR  = 2;
-PT   = 10.^(SNR/10);
-Data = struct;
-for i = 1:numel(NR2)
-    [G,Hd,Hr,omega] = get_loc_pathloss_csi(K,Nt,NR2(i)); % produce the targeted problem, for b=2
-    Data(i).b     = b;
-    Data(i).PT    = PT;
-    Data(i).G     = G;
-    Data(i).Hd    = Hd;
-    Data(i).Hr    = Hr;
-    Data(i).omega = omega;
-end
-save('Beanforming.mat','Data');
-end
\ No newline at end of file
diff --git a/matlab/Problems/Real-World/Beanforming/get_loc_pathloss_csi.m b/matlab/Problems/Real-World/Beanforming/get_loc_pathloss_csi.m
deleted file mode 100644
index 72789c9..0000000
--- a/matlab/Problems/Real-World/Beanforming/get_loc_pathloss_csi.m
+++ /dev/null
@@ -1,119 +0,0 @@
-%% 
-function  [G, Hd, Hr, omega] = get_loc_pathloss_csi(K, Nt, NR2)
-%% generate new location for the K users
-Pt=zeros(K,2);
-%%
-Lroom=100; %Ĭ��200
-Wroom=30; %Ĭ��30
-k1=[1,0];
-k2=[0,1];
-R=10;
-%%
-for k0=1:K
-    r=rand(1,1)*R;
-    theta=rand(1,1)*2*pi;
-    px=r*cos(theta);
-    py=r*sin(theta);
-    pt=[Lroom,Wroom]+px*k1+py*k2;
-    Pt(k0,:)=pt;
-end
-%%
-%save('user_location.mat','K','Pt','Lroom','Wroom','R');
-
-plot_ind=0;
-if plot_ind==1
-    figure
-    plot(Pt(:,1),Pt(:,2),'ro');
-    xlim([Lroom-R,Lroom+R]);ylim([Wroom-R,Wroom+R]);
-    hold on
-    theta=linspace(0,1,100).*2.*pi; %�뾶100
-    hold on
-    plot(Lroom+R*cos(theta),Wroom+R*sin(theta),'r.')
-end
-
-%-------------------------------------------------------------------------
-%% compute the pathloss according to the users' locations
-%% 
-AP=[0,0];
-%%
-IRS=[100,0];
-d_g=sqrt(sum(abs(AP-IRS).^2));
-L_g=path_LOS( d_g );
-%% IRS-assist link
-Lu=zeros(1,K);
-for k0=1:K
-    pt=Pt(k0,:)-IRS;
-    du=sqrt(sum(abs(pt).^2));
-    Lu(k0)=path_LOS( du );
-end
-Lu=Lu+L_g;
-%% direct link
-Ld=zeros(1,K);
-for k0=1:K
-    pt=Pt(k0,:)-AP;
-    dk=sqrt(sum(abs(pt).^2));
-    Ld(k0)=path_NLOS( dk );
-%     Ld_test(k0)=path_LOS( dk );
-end
-%Lu-Ld
-%%
-%save('user_pathloss.mat','K','Lu','Ld');
-
-%-------------------------------------------------------------------------
-%% Generate the channel coefficients
-noise=-170+10*log10(180*1e3); %�������Խ��sum rateԽ��
-path_d=10.^((-noise-Ld)/10);
-path_i=10.^((-noise-Lu)/10);
-%%
-ite=1; 
-%%
-%%
-pd=sqrt(path_d);
-pd=repmat(pd.',1,Nt);
-ps=sqrt(path_i);
-ps=repmat(ps.',1,NR2);
-%% theta_init, channel Hd
-Hd_w=zeros(K,Nt,ite);
-
-for j0=1
-    Hd=sqrt(1/2).*(randn(K,Nt)+1j.*randn(K,Nt));
-    %%
-    Hd_w(:,:,j0)=Hd;
-end
-% load('CSI8(20,20)Hd_NR2_10.mat','K','NR2','Nt','Pt',...
-%   'pd','ps','Hd','AP_angle','IRS_angle',...
-% 'User_angle','Hd');
-
-%%
-eb=10;
-eb2=1/(1+eb);
-eb1=1-eb2;
-eb1=sqrt(eb1);
-eb2=sqrt(eb2);
-%% channel G
-AP_angle=rand(1,1);
-IRS_angle=rand(1,1);
-G_sig=zeros(NR2,Nt,ite);
-for i0=1:ite
-    G_sig(:,:,i0)=sqrt(1/2).*(randn(NR2,Nt)+1j.*randn(NR2,Nt));
-end
-%% channel Hr_w
-User_angle=rand(1,K);
-Hr_sig=zeros(K,NR2,ite);
-for i0=1:ite
-    Hr_sig(:,:,i0)=sqrt(1/2).*(randn(K,NR2)+1j.*randn(K,NR2));
-end
-
-Hd=pd.*Hd_w(:,:,1);
-G=channel_G(AP_angle,IRS_angle,G_sig(:,:,1),eb1,eb2,NR2,Nt);
-Hr=ps.*channel_Hr(User_angle,Hr_sig(:,:,1),eb1,eb2,K,NR2);
-
-% Generate weights of users ����omega
-% weight=1./((path_d));
-% omega=weight./sum(weight);
-omega=ones(1, K);
-%%
-% save('CSI8(20,20)Hd_NR2_10.mat','K','NR2','Nt','Pt',...
-%   'pd','ps','Hd','AP_angle','IRS_angle',...
-% 'User_angle','Hd','Hr','G');
-end
\ No newline at end of file
diff --git a/matlab/Problems/Real-World/Beanforming/get_sum_rate.m b/matlab/Problems/Real-World/Beanforming/get_sum_rate.m
deleted file mode 100644
index 941f46a..0000000
--- a/matlab/Problems/Real-World/Beanforming/get_sum_rate.m
+++ /dev/null
@@ -1,24 +0,0 @@
-function sumRs = get_sum_rate(ms,b,Hd,Hr,G,PT,omega)
-[~, Nt]=size(G);
-m_num=size(ms,1);
-sumRs=zeros(1, m_num);
-Qs = exp(1i*ms/(2^b)*2*pi);
-
-for mi=1:m_num
-    %% get hybrid CSI: F        
-    F= Hd+Hr*diag(Qs(mi,:)')*G;
-    
-    %% get BS beamforming: VD
-    W=F'/(F*F');
-    vk=diag(W'*W);
-    [vk_fill] = water_filling(PT, vk);
-    PKs=vk_fill./vk;
-    VD=W.*repmat(sqrt(PKs'),Nt,1);
-    
-    %% get sum rate
-    He = abs(F * VD).^2;
-    FV_K_others = sum(He,2) - diag(He);
-    R = diag(He)./( FV_K_others + 1);
-    sumRs(mi) = 1./sum(log(R+1).*omega');
-end
-end
\ No newline at end of file
diff --git a/matlab/Problems/Real-World/Beanforming/path_LOS.m b/matlab/Problems/Real-World/Beanforming/path_LOS.m
deleted file mode 100644
index c2ce8db..0000000
--- a/matlab/Problems/Real-World/Beanforming/path_LOS.m
+++ /dev/null
@@ -1,9 +0,0 @@
-function [ loss ] = path_LOS( d )
-%     d=d/1000;
-%     loss=89.5 + 16.9*log10(d); 
-%     loss=38.46 + 20*log10(d);
-%     loss=35.6 + 22*log10(d); %Ĭ�ϡ���ֵԽ�󣬴���path lossԽ��˥��Խ���أ�sum rateԽС��
-% loss=20 + 20*log10(d); %��������������WSR
-loss=20 + 20*log10(d);%20��������
-end 
-
diff --git a/matlab/Problems/Real-World/Beanforming/path_NLOS.m b/matlab/Problems/Real-World/Beanforming/path_NLOS.m
deleted file mode 100644
index 21da859..0000000
--- a/matlab/Problems/Real-World/Beanforming/path_NLOS.m
+++ /dev/null
@@ -1,9 +0,0 @@
-function [ loss ] = path_NLOS( d )
-%     d=d/1000;
-%     loss=147.4 +43.3*log10(d);   
-%     loss=max(15.3 +37.6*log10(d), path_LOS( d ))+20; 
-%     loss=max(2.7 +42.8*log10(d), path_LOS( d ))+20;  
-%     loss=32.6 +36.7*log10(d);
-      loss=32.6 +36.7*log10(d);
-end
-
diff --git a/matlab/Problems/Real-World/Beanforming/water_filling.m b/matlab/Problems/Real-World/Beanforming/water_filling.m
deleted file mode 100644
index fd7c101..0000000
--- a/matlab/Problems/Real-World/Beanforming/water_filling.m
+++ /dev/null
@@ -1,25 +0,0 @@
-function [vk_fill] = water_filling(PT, vk)
-% filling line: miu
-K=numel(vk);
-miu=max(vk);
-if sum(miu-vk)>PT
-    [vkd,~]=sort(vk,'descend');
-    t=1;
-    vka_sum=sum(miu-vk);
-    while vka_sum>PT && t<numel(vk)
-        miu=vkd(t+1);
-        vka_sum=sum(max(miu-vk,0));
-        if vka_sum<=PT
-            miu=miu+(PT-vka_sum)/(K-t);
-            break;
-        end
-        t=t+1;
-    end
-    vk_fill=max(miu-vk,0);
-    
-else
-    miu=miu+(PT-sum(miu-vk))/K;
-    vk_fill=miu-vk;
-end
-end
-
diff --git a/matlab/Problems/Real-World/PBO/pbo.m b/matlab/Problems/Real-World/PBO/pbo.m
deleted file mode 100644
index 88914d6..0000000
--- a/matlab/Problems/Real-World/PBO/pbo.m
+++ /dev/null
@@ -1,78 +0,0 @@
-function [output1,output2,output3] = pbo(varargin)
-% The beanforming problem in RIS-aided communications.
-
-%------------------------------Reference-----------------------------------
-% Yan B, Zhao Q, Li M, et al. Fitness landscape analysis and niching 
-% genetic approach for hybrid beamforming in RIS-aided communications[J]. 
-% Applied Soft Computing, 2022, 131: 109725.
-%------------------------------Copyright-----------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-switch varargin{end}
-    case 'construct' % define problem properties
-        Problem  = varargin{1};
-        instance = varargin{2};       
-        Data     = struct('o',[]);
-        for i = 1:length(instance)
-            if instance(i) == 1
-                D = 100;
-            elseif instance(i) == 2
-                D = 225;
-            elseif instance(i) == 3
-                D = 400;
-            elseif instance(i) == 4
-                D = 625;
-            elseif instance(i) == 5
-                D = 90;
-            else
-                error('Only instances 1, 2, and 3 are available.')
-            end  
-            
-            lower = zeros(1,D);
-            upper = zeros(1,D)+1;
-            
-            Problem(i).type = {'discrete','static','certain'};
-            Problem(i).bound = [lower;upper];
-            %Problem(i).problem_index = 1
-            Data(i).o = Data; % do nothing but add data(i)
-        end
-        
-        output1 = Problem;
-        output2 = Data;
-
-    case 'repair' % repair solutions
-        Decs = varargin{2};
-        output1 = Decs;
-    
-    case 'evaluate' % evaluate solution's fitness
-        problem_id  = varargin{3};
-        Decs = varargin{2};
-        [q,dim] = size(Decs);
-        res = py.IOH_Test.PBO(Decs,dim,problem_id);
-        res = -double(res).';
-        output1 = res; % matrix for saving objective function values
-end
-
-if ~exist('output2','var')
-    output2 = [];
-end
-if ~exist('output3','var')
-    output3 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Problems/Real-World/Power Dispatch/DataPowerDispatch.xlsx b/matlab/Problems/Real-World/Power Dispatch/DataPowerDispatch.xlsx
deleted file mode 100644
index 0c66f08..0000000
Binary files a/matlab/Problems/Real-World/Power Dispatch/DataPowerDispatch.xlsx and /dev/null differ
diff --git a/matlab/Problems/Real-World/Power Dispatch/PowerDispatch.m b/matlab/Problems/Real-World/Power Dispatch/PowerDispatch.m
deleted file mode 100644
index 8217de3..0000000
--- a/matlab/Problems/Real-World/Power Dispatch/PowerDispatch.m	
+++ /dev/null
@@ -1,49 +0,0 @@
-function [output1,output2,output3] = PowerDispatch(varargin)
-switch varargin{end}
-    case 'construct' % construct problem and data
-        Problem = varargin{1};
-        instance = varargin{2};
-
-        orgData = readmatrix('DataPowerDispatch.xlsx','Sheet',1);
-        Data = struct('orgData',[]);
-        
-        for i = 1:numel(instance)
-            Problem(i) = Problem(1); 
-            Data(i).orgData = orgData;
-            Problem(i).bound = [orgData(:,4)';orgData(:,5)']; % bound constraint
-        end
-
-        output1 = Problem(instance);
-        output2 = Data(instance);
-
-    case 'repair'
-        Decs = varargin{2};
-        output1 = Decs;
-    
-    case 'evaluate' % evaluate fitness
-        Data = varargin{1}.orgData;
-        Decs = varargin{2}; % solution set
-
-        N = size(Decs,1); % population size
-        Objs = zeros(N,1); % fitness of solutions
-        for i = 1:N
-            solution = Decs(i,:); % 1*|generator|
-            F = zeros(1,size(Data,1)); % fuel cost of generators, 1*|generator|
-            for j = 1:size(Data,1) % for each generator
-                F(j) = Data(j,1) + Data(j,2)*solution(j) + Data(j,3)*solution(j)^2; % objective function
-            end 
-            Objs(i) = sum(F);
-            if sum(solution) < Data(1,6) % power balance constraint 
-               Objs(i) = 10^6; % assign an extreme large fitness value to infeasible solution  
-            end
-        end
-        output1 = Objs;
-end
-
-if ~exist('output2','var')
-    output2 = [];
-end
-if ~exist('output3','var')
-    output3 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Problems/Real-World/Warehouse Management/Assign.m b/matlab/Problems/Real-World/Warehouse Management/Assign.m
deleted file mode 100644
index 1a9a71f..0000000
--- a/matlab/Problems/Real-World/Warehouse Management/Assign.m	
+++ /dev/null
@@ -1,39 +0,0 @@
-%% load data
-filename = 'DataProcessed.xlsx';
-[Data,~,~] = xlsread(filename);
-
-%% rank products according to their future picking up frequencies, ranks are products' locations
-ProdInd = unique(Data(:,1)); % all products
-NumProd = length(ProdInd); % number of products
-MaterInd = unique(Data(:,2)); % all materials
-fr = zeros(NumProd,1); % future picking up frequency of p
-for i = 1: NumProd
-   fr(i) = sum(Data(Data(:,1)==ProdInd(i),6)); 
-end
-[~,loc] = sort(fr,'descend'); % locations of products, corresponding to products' indexes
-temp = zeros(NumProd,1);
-for i = 1:NumProd
-    temp(i) = find(loc==i);
-end
-loc = temp;
-
-%% assign each common material to a product, such that this common material's overall picking up distance is minimum
-for i = 1:length(MaterInd)
-    frMater = Data(Data(:,2)==MaterInd(i),6); % future picking up frequencies of material i 
-    if length(frMater) > 1 % if common material
-        ProdInd_ComMater = Data(Data(:,2)==MaterInd(i),1); % indexes of products that use the common material
-        dis = sum(repmat(frMater,1,NumProd) .* abs(repmat(loc(ProdInd_ComMater),1,NumProd) - repmat(loc',length(frMater),1)),1); % 1:NumProd
-        [~,best] = min(dis);
-        Data(Data(:,2)==MaterInd(i),1) = ProdInd(best); % assign a new product for the common material
-    end
-end
-
-%% delete repetitive material records
-for i = 1:length(MaterInd)
-    repMaterLine = find(Data(:,2)==MaterInd(i));
-    Data(repMaterLine(2:end),:) = [];
-end
-
-%% save data
-xlswrite('DataAssigned.xlsx',Data,'Sheet1');
-xlswrite('DataAssigned.xlsx',loc,'Sheet2');
\ No newline at end of file
diff --git a/matlab/Problems/Real-World/Warehouse Management/DataAssigned.xlsx b/matlab/Problems/Real-World/Warehouse Management/DataAssigned.xlsx
deleted file mode 100644
index 999363e..0000000
Binary files a/matlab/Problems/Real-World/Warehouse Management/DataAssigned.xlsx and /dev/null differ
diff --git a/matlab/Problems/Real-World/Warehouse Management/DataClearned.xlsx b/matlab/Problems/Real-World/Warehouse Management/DataClearned.xlsx
deleted file mode 100644
index 7a539e8..0000000
Binary files a/matlab/Problems/Real-World/Warehouse Management/DataClearned.xlsx and /dev/null differ
diff --git a/matlab/Problems/Real-World/Warehouse Management/DataOrg.xlsx b/matlab/Problems/Real-World/Warehouse Management/DataOrg.xlsx
deleted file mode 100644
index e83db13..0000000
Binary files a/matlab/Problems/Real-World/Warehouse Management/DataOrg.xlsx and /dev/null differ
diff --git a/matlab/Problems/Real-World/Warehouse Management/DataProcessed.xlsx b/matlab/Problems/Real-World/Warehouse Management/DataProcessed.xlsx
deleted file mode 100644
index 38d015c..0000000
Binary files a/matlab/Problems/Real-World/Warehouse Management/DataProcessed.xlsx and /dev/null differ
diff --git a/matlab/Problems/Real-World/Warehouse Management/DataStacked.mat b/matlab/Problems/Real-World/Warehouse Management/DataStacked.mat
deleted file mode 100644
index dd849d8..0000000
Binary files a/matlab/Problems/Real-World/Warehouse Management/DataStacked.mat and /dev/null differ
diff --git a/matlab/Problems/Real-World/Warehouse Management/Place.m b/matlab/Problems/Real-World/Warehouse Management/Place.m
deleted file mode 100644
index c967d25..0000000
--- a/matlab/Problems/Real-World/Warehouse Management/Place.m	
+++ /dev/null
@@ -1,137 +0,0 @@
-function [output1,output2,output3] = Place(varargin)
-maxH = 200;
-switch varargin{end}
-    case 'construct' % construct problem and data
-        type     = {'permutation','sequential','certain'};
-        Problem  = varargin{1};
-        instance = varargin{2};
-
-        orgData = load('DataStacked.mat');
-        Data = struct('orgData',[],'rackH',[],'maxH',[],'rackInd',[],'prodInd',[],'preSolRack',[],'preSolProd',[],'continue',[]);
-        
-        lowerH = 20;
-        upperH = 150;
-        intervalH = 10;
-        H = (lowerH:intervalH:upperH)';
-        numH = length(H);
-        rackSize = [repmat(180,numH,1),repmat(60,numH,1),H;repmat(240,numH,1),repmat(120,numH,1),H;]; % sizes of all kinds of racks, [long,width,height]
-        rackH = rackSize(:,3);
-        
-        numInstance = size(orgData.usedRacks,2);
-        for i = 1:numInstance
-            Problem(i) = Problem(1); % a number of numProd problem instances
-            Problem(i).type = type;
-            Data(i).orgData = orgData.usedRacks(:,i); % cells, each collects indexes (can be repetitive) of all racks used for one product         
-            Data(i).rackH   = rackH;
-            Data(i).maxH    = maxH;
-            Data(i).prodInd = 1; % index of the current considered product
-            Data(i).rackInd = Data(i).orgData{Data(i).prodInd}; % indexes of racks that the current product used
-
-            totalH = sum(Data(i).rackH(Data(i).rackInd));
-            Data(i).preSolRack = [];
-            Data(i).preSolProd = [];
-            while totalH < maxH && Data(i).prodInd < length(Data(i).orgData)
-                Data(i).preSolRack = [Data(i).preSolRack,Data(i).rackInd]; % a part of the solution (rack indexes) to the current problem
-                Data(i).preSolProd = [Data(i).preSolProd,repmat(Data(i).prodInd,1,numel(Data(i).rackInd))]; % a part of the solution (product indexes) to the current problem
-                Data(i).maxH = maxH-totalH; % vertical height being considered in the current problem
-                Data(i).prodInd = Data(i).prodInd+1; % index of the current considered product
-                Data(i).rackInd = Data(i).orgData{Data(i).prodInd};
-                totalH = totalH+sum(Data(i).rackH(Data(i).rackInd));
-            end
-
-            Problem(i).bound = [1;numel(Data(i).rackInd)];
-
-            if Data(i).prodInd == length(Data(i).orgData) && totalH <= maxH
-                Data(i).continue = false;
-            else
-                Data(i).continue = true;
-            end
-        end
-
-        output1 = Problem(instance);
-        output2 = Data(instance);
-
-    case 'repair'
-        Decs = varargin{2};
-        output1 = Decs;
-    
-    case 'evaluate' % evaluate fitness
-        Data = varargin{1};
-        Decs = varargin{2};
-
-        N = size(Decs,1); % population size
-        D = size(Decs,2); % deminsion of solution, i.e., number of racks
-        Objs = zeros(N,1);
-        acc1 = zeros(N,1);
-        acc2 = cell(N,1); % explicit solutions, i.e., racks' placement
-        for i = 1: N
-            currH = 0;
-            j = 1;
-            k = [];
-            while currH < Data.maxH && j <= D
-                currH = currH+Data.rackH(Data.rackInd(Decs(i,j)));
-                k = [k,Data.rackInd(Decs(i,j))]; % indexes of racks that have been placed
-                if currH > Data.maxH
-                    currH = currH-Data.rackH(Data.rackInd(Decs(i,j)));
-                    k(end) = []; % delete the rack that cannot be placed
-                end
-                j = j+1;
-            end
-
-            Objs(i) = Data.maxH - currH;
-            acc2{i}(1,:) = [Data.preSolRack,k]; % all racks placed in the current column
-            acc2{i}(2,:) = [Data.preSolProd,repmat(Data.prodInd,1,numel(k))]; % each rack's product index
-        end
-        output1 = Objs;
-        output3 = {acc1,acc2};
-
-    case 'sequence' % change time step in the problem sequence
-        Problem = varargin{1};
-        Data = varargin{2};
-        solution = varargin{3};
-        
-        Data.maxH = maxH;
-        usedRackInd = solution.acc{2}(1,:);
-        usedRackInd = usedRackInd(solution.acc{2}(2,:)==Data.prodInd); % current product's used racks
-        for i = 1:numel(usedRackInd)
-            sameInd = find(Data.rackInd==usedRackInd(i));
-            Data.rackInd(sameInd(1)) = [];
-        end
-
-        totalH = sum(Data.rackH(Data.rackInd));
-        Data.preSolRack = [];
-        Data.preSolProd = [];
-        while totalH < maxH && Data.prodInd < length(Data.orgData)
-            Data.preSolRack = [Data.preSolRack,Data.rackInd]; % a part of the solution (rack indexes) to the current problem
-            Data.preSolProd = [Data.preSolProd,repmat(Data.prodInd,1,numel(Data.rackInd))]; % a part of the solution (product indexes) to the current problem
-            Data.maxH = maxH-totalH; % vertical height being considered in the current problem
-            Data.prodInd = Data.prodInd+1; % index of the current considered product
-            Data.rackInd = Data.orgData{Data.prodInd};
-            totalH = totalH+sum(Data.rackH(Data.rackInd));
-        end
-
-        while numel(Data.rackInd) == 1 && Data.prodInd < length(Data.orgData) % if only one rack for placement
-            Data.prodInd = Data.prodInd+1;
-            Data.rackInd = [Data.rackInd,Data.orgData{Data.prodInd}];
-            totalH = sum(Data.rackH(Data.rackInd));
-        end
-
-        Problem.bound = [1;numel(Data.rackInd)];
-
-        if Data.prodInd == length(Data.orgData) && totalH <= maxH
-            Data.continue = false;
-        else
-            Data.continue = true;
-        end
-
-        output1 = Problem;
-        output2 = Data;
-end
-
-if ~exist('output2','var')
-    output2 = [];
-end
-if ~exist('output3','var')
-    output3 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Problems/Real-World/Warehouse Management/ProcessData.m b/matlab/Problems/Real-World/Warehouse Management/ProcessData.m
deleted file mode 100644
index 9fe74bd..0000000
--- a/matlab/Problems/Real-World/Warehouse Management/ProcessData.m	
+++ /dev/null
@@ -1,66 +0,0 @@
-% prepare
-days = 7;
-filename = 'DataOrg.xlsx';
-
-% elminate null
-[~,~,raw] = xlsread(filename);
-raw(strcmp(raw(:,3),'ActiveX VT_ERROR: '),:) = []; % null in size
-raw(strcmp(raw(:,4),'ActiveX VT_ERROR: '),:) = []; % null in size
-raw(strcmp(raw(:,5),'ActiveX VT_ERROR: '),:) = []; % null in size
-raw(strcmp(raw(:,6),'ActiveX VT_ERROR: '),:) = []; % null in frequency
-raw(strcmp(raw(:,7),'ActiveX VT_ERROR: '),:) = []; % null in SKU
-
-% elminate zeros
-for i = 3:7 
-    tempLoc = false(size(raw,1),1);
-    for j = 1:size(raw,1)
-        if raw{j,i}==0
-            tempLoc(j) = true;
-        end
-    end
-    raw(tempLoc,:) = [];
-end
-
-xlswrite('DataClearned.xlsx',raw,'Sheet1'); % for restore to original names of products and materials
-
-raw(1,:) = [];
-raw(:,8) = [];
-
-% keep all cells being strings
-for i = 1:size(raw,1)
-    for j = 1:size(raw,2)
-        raw{i,j} = num2str(raw{i,j}); 
-    end
-end
-
-% give products numerical indexes
-IndProd = raw(:,1);
-temp = unique(IndProd);
-NumIndProd = zeros(length(IndProd),1);
-for i = 1:length(temp)
-    NumIndProd(strcmp(IndProd,temp(i))) = i; % numerical indexes of products
-end
-
-% give materials numerical indexes
-IndMater = raw(:,2);
-temp = unique(IndMater);
-NumIndMater = zeros(length(IndMater),1);
-for i = 1:length(temp)
-    NumIndMater(strcmp(IndMater,temp(i))) = i; % numerical indexes of materials
-end
-
-% load size and frequency data
-NumSizeFr = str2double(raw(:,3:7)); % strings in multiple cells to doubles in a single matrix
-
-data = [NumIndProd,NumIndMater,NumSizeFr]; % data with indexes of products and materials
-
-% calculate SKU (boxes)
-data(:,6) = data(:,6)./30.*days; % picking up frequencies
-filename = 'Size.xlsx';
-[~,~,rawSize] = xlsread(filename);
-for i = 1:length(temp)
-    NumMaterBox = rawSize{strcmp(rawSize(:,1),temp(i)),2}; % number of materials in one box
-    data(strcmp(IndMater,temp(i)),7) = ceil(data(strcmp(IndMater,temp(i)),7)./NumMaterBox); % update SKU (boxes), common material has the same SKU value in each line of the material
-end
-writecell
-xlswrite('DataProcessed.xlsx',data,'Sheet1');
\ No newline at end of file
diff --git a/matlab/Problems/Real-World/Warehouse Management/Stack.m b/matlab/Problems/Real-World/Warehouse Management/Stack.m
deleted file mode 100644
index 574a64e..0000000
--- a/matlab/Problems/Real-World/Warehouse Management/Stack.m	
+++ /dev/null
@@ -1,184 +0,0 @@
-function [output1,output2,output3] = Stack(varargin)
-switch varargin{end}
-    case 'construct' % % construct problem and data
-        type     = {'discrete','static','certain'};
-        Problem  = varargin{1};
-        instance = varargin{2};
-
-        orgData = readmatrix('DataAssigned.xlsx','Sheet',1);
-        prodInd = unique(orgData(:,1)); % all products
-        numProd = length(prodInd);
-        Data = struct('orgData',[],'rackSize',[],'stackSt',[],'inFesMater',[],'cons',[]);
-
-        lowerH = 20;
-        upperH = 150;
-        intervalH = 10;
-        H = (lowerH:intervalH:upperH)';
-        numH = length(H);
-        rackSize = [repmat(180,numH,1),repmat(60,numH,1),H;repmat(240,numH,1),repmat(120,numH,1),H;]; % sizes of all kinds of racks, [long,width,height]
-        
-        for i = 1:numProd
-            Problem(i) = Problem(1); % a number of numProd problem instances
-            Problem(i).type = type;
-            Data(i).orgData = orgData(orgData(:,1)==prodInd(i),:); % original data about the materials of product i
-            
-            % materials' stacking strategies when using each kind of racks
-            boxSize = Data(i).orgData(:,3:5);  % size of boxes of all materials of product i, [long,width,height]
-            numMater = size(Data(i).orgData,1); % number of materials
-            stackSt = cell(numMater,1);
-            cons = cell(numMater,1);
-            inFesMater = false(numMater,1);
-            for j = 1:numMater
-                NH = floor(rackSize(:,3)./boxSize(j,3));
-                NW = zeros(size(rackSize,1),1);
-                NL = zeros(size(rackSize,1),1);
-                control = zeros(size(rackSize,1),1); % control materials' stacking directions, 1: length side of material stacks in width side of rack, 2: width side of material stacks in width side of rack
-                for k = 1:size(rackSize,1)
-                    if rackSize(k,2) < min(boxSize(j,1),boxSize(j,2))
-                        NW(k) = 0;
-                    elseif rackSize(k,2) >= max(boxSize(j,1),boxSize(j,2))
-                        if rem(rackSize(k,2),boxSize(j,1)) < rem(rackSize(k,2),boxSize(j,2)) % length side of material stacks in width side of rack
-                            NW(k) = floor(rackSize(k,2)./boxSize(j,1));
-                            control(k) = 1;
-                        else
-                            NW(k) = floor(rackSize(k,2)./boxSize(j,2));
-                            control(k) = 2;
-                        end
-                    elseif rackSize(k,2) < boxSize(j,1) && rackSize(k,2) >= boxSize(j,2) % width side of material stacks in width side of rack
-                        NW(k) = floor(rackSize(k,2)./boxSize(j,2));
-                        control(k) = 2;
-                    elseif rackSize(k,2) >= boxSize(j,1) && rackSize(k,2) < boxSize(j,2) % length side of material stacks in width side of rack
-                        NW(k) = floor(rackSize(k,2)./boxSize(j,1));
-                        control(k) = 1;
-                    end
-                    if NW(k) == 0 || NH(k) == 0
-                        NL(k) = 0;
-                    else
-                        NL(k) = ceil(Data(i).orgData(j,7)./NW(k)./NH(k));
-                        if control(k) == 1
-                            total_L =  boxSize(j,2)*NL(k);
-                        elseif control(k) == 2
-                            total_L =  boxSize(j,1)*NL(k);
-                        end
-                        if total_L > rackSize(k,1)
-                            NL(k) = 0;
-                        end
-                    end
-                end
-                stackSt{j} = [NL,NW,NH,control]; % 28*4
-                cons{j} = NL.*NW.*NH==0; % 28*1 logical
-                if prod(sum(stackSt{j}(:,1:3))) == 0
-                    inFesMater(j) = true; % indexes of materials that cannot be stacked on all kinds of racks
-                end
-            end
-            Data(i).rackSize = rackSize;
-            Data(i).stackSt = stackSt; % product i's materials' stacking strategies
-            Data(i).inFesMater = inFesMater; % indexes of materials that cannot be stacked on all kinds of racks
-            Data(i).cons = cons;
-
-            Data(i).orgData(inFesMater,:) = []; % delete unstacked materials
-            Data(i).stackSt(inFesMater) = [];
-            Data(i).cons(inFesMater) = [];
-
-            D = size(Data(i).orgData,1); % dimension of decision space
-            Problem(i).bound = [ones(1,D);repmat(size(rackSize,1),1,D)]; % product i's decision space's boundry, i.e., (min, max) indexes of kinds of racks being used
-        end
-
-        output1 = Problem(instance);
-        output2 = Data(instance);
-
-    case 'repair'
-        Data = varargin{1};
-        Decs = varargin{2};
-        D    = size(Decs,2);
-        Cons = Data.cons; % D*1 cells, each cell is an |Upper-Lower|*1 logic matrix
-        for i = 1:D
-            InFesValue = find(Cons{i}==1);
-            FesValue = find(Cons{i}==0);
-            for j = 1:length(InFesValue)
-                Decs(Decs(:,i)==InFesValue(j),i) = FesValue(randperm(length(FesValue),1));
-            end
-        end
-        output1 = Decs;
-    
-    case 'evaluate' % % evaluate fitness
-        Data = varargin{1};
-        Decs = varargin{2};
-
-        N = size(Decs,1); % population size
-        D = size(Decs,2); % deminsion of solution, i.e., number of materials
-        Objs = zeros(N,1);
-        WasteL = zeros(N,1); % totally waste lengthes of all racks used for the given product
-        NumRacks = cell(N,1); % number of eack kind of used racks
-
-        materSize = Data.orgData(:,3:5); % |materials|*3, all materials' sizes
-        materVol = prod(materSize,2).*Data.orgData(:,7); % |materials|*1, volumn of all boxes of each material (SKU boxes)
-        numRack = size(Data.rackSize,1);
-        materL = zeros(numRack,D); % 28*|materials|, length side of each material in each kind of racks
-        NL = zeros(numRack,D); % 28*|materials|, number of materials stacked along the length side of each kind of racks
-        for i = 1:D % for each material
-            materL(Data.stackSt{i}(:,4)==1,i) = materSize(i,2);
-            materL(Data.stackSt{i}(:,4)==2,i) = materSize(i,1);
-            NL(:,i) = Data.stackSt{i}(:,1);
-        end
-        rackL = Data.rackSize(:,1); % 28*1, lengthes of all kinds of racks
-        rackVol = prod(Data.rackSize,2); % 28*1, volumns of all kinds of racks
-
-        for i = 1: N
-            solution = Decs(i,:); % index of rack being used by all materials of a given product
-            rackInd = unique(solution); % indexes of kinds of racks being used
-            C = zeros(length(rackInd),1);
-            Clength = zeros(length(rackInd),1);
-            NumRack =  zeros(length(rackInd),1);
-            for j = 1:length(rackInd)
-                j_materL   = materL(rackInd(j),solution==rackInd(j)); % length side of the materials (belong to the given product) that use rack j
-                j_NL       = NL(rackInd(j),solution==rackInd(j)); % number of materials stacked along the length side of rack j
-                j_materVol = materVol(solution==rackInd(j));
-                j_rackL    = rackL(rackInd(j)); % length of rack j
-                j_rackVol  = rackVol(rackInd(j)); % volumn of rack j
-
-                if sum(j_materL.*j_NL) <= j_rackL
-                    C(j) = j_rackVol-sum(j_materVol);
-                    Clength(j) = j_rackL-sum(j_materL.*j_NL);
-                    NumRack(j) = NumRack(j)+1;
-                else
-                    j_materSize = materSize(solution==rackInd(j),:); % size of single box of the materials that use rack j
-                    [~,ind] = sort(prod(j_materSize,2),'descend');
-                    k = 1; % counter of materials that use rack j
-                    currL = 0;
-                    currVol = 0;
-                    C(j) = 0;
-                    while k <= length(j_materL) % while each material
-                        while currL < j_rackL && k <= length(j_materL)
-                            currL = currL+j_materL(ind(k))*j_NL(ind(k));
-                            currVol = currVol+j_materVol(ind(k));
-                            k = k+1;
-                        end
-                        if currL > j_rackL
-                            currL = currL-sum(j_materL(ind(k-1))*j_NL(ind(k-1)));
-                            currVol = currVol-j_materVol(ind(k-1));
-                            k = k-1;
-                        end
-                        C(j) = C(j)+(j_rackVol-currVol);
-                        Clength(j) = Clength(j)+(j_rackL-currL);
-                        NumRack(j) = NumRack(j)+1;
-                        currL = 0;
-                        currVol = 0;
-                    end
-                end
-            end
-            Objs(i) = sum(C);
-            WasteL(i) = sum(Clength);
-            NumRacks{i} = NumRack;
-        end
-        output1 = Objs;
-        output3 = {WasteL,NumRacks};
-end
-
-if ~exist('output2','var')
-    output2 = [];
-end
-if ~exist('output3','var')
-    output3 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Problems/Real-World/Warehouse Management/StackTraverse.m b/matlab/Problems/Real-World/Warehouse Management/StackTraverse.m
deleted file mode 100644
index 3a666c6..0000000
--- a/matlab/Problems/Real-World/Warehouse Management/StackTraverse.m	
+++ /dev/null
@@ -1,75 +0,0 @@
-function [Objs,WasteL,NumRacks] = Stack(Data,Decs)
-N = size(Decs,1); % population size
-D = size(Decs,2); % deminsion of solution, i.e., number of materials
-Objs = zeros(N,1);
-WasteL = zeros(N,1); % totally waste lengthes of all racks used for the given product
-NumRacks = cell(N,1); % number of eack kind of used racks
-
-materSize = Data.orgData(:,3:5); % |materials|*3, all materials' sizes
-materVol = prod(materSize,2).*Data.orgData(:,7); % |materials|*1, volumn of all boxes of each material (SKU boxes)
-numRack = size(Data.rackSize,1);
-materL = zeros(numRack,D); % 28*|materials|, length side of each material in each kind of racks
-NL = zeros(numRack,D); % 28*|materials|, number of materials stacked along the length side of each kind of racks
-for d = 1:D % for each material
-    materL(Data.stackSt{d}(:,4)==1,d) = materSize(d,2);
-    materL(Data.stackSt{d}(:,4)==2,d) = materSize(d,1);
-    NL(:,d) = Data.stackSt{d}(:,1);
-end
-rackL = Data.rackSize(:,1); % 28*1, lengthes of all kinds of racks
-rackVol = prod(Data.rackSize,2); % 28*1, volumns of all kinds of racks
-
-for i = 1: N  
-    solution = Decs(i,:); % index of rack being used by all materials of a given product
-    rackInd = unique(solution); % indexes of kinds of racks being used
-    C = zeros(length(rackInd),1); 
-    Clength = zeros(length(rackInd),1); 
-    NumRack =  zeros(length(rackInd),1); 
-    for j = 1:length(rackInd)
-        j_materL   = materL(rackInd(j),solution==rackInd(j)); % length side of a single box of each material (belong to the given product) that uses rack j
-        j_NL       = NL(rackInd(j),solution==rackInd(j)); % number of materials stacked along the length side of  rack j
-        j_materVol = materVol(solution==rackInd(j));
-        j_rackL    = rackL(rackInd(j)); % length of rack j
-        j_rackVol  = rackVol(rackInd(j)); % volumn of rack j
-        
-        if sum(j_materL.*j_NL) <= j_rackL
-            C(j) = j_rackVol-sum(j_materVol);
-            Clength(j) = j_rackL-sum(j_materL.*j_NL);
-            NumRack(j) = NumRack(j)+1;
-        else
-            j_materSize = materSize(solution==rackInd(j),:); % size of single box of the materials that use rack j
-            [~,ind] = sort(prod(j_materSize,2),'descend');
-            k = 1;
-            stackedMaterInd = [];
-            currL = 0;
-            currVol = 0;
-            C(j) = 0;
-            while ~isempty(ind) % while each material
-                while currL < j_rackL && k <= numel(ind) % while each unstacked materials
-                    currL = currL+j_materL(ind(k))*j_NL(ind(k));
-                    currVol = currVol+j_materVol(ind(k));
-                    stackedMaterInd = [stackedMaterInd,ind(k)]; 
-                    if currL > j_rackL
-                        currL = currL-sum(j_materL(ind(k))*j_NL(ind(k)));
-                        currVol = currVol-j_materVol(ind(k));
-                        stackedMaterInd(end) = [];
-                    end
-                    k = k+1;
-                end
-
-                C(j) = C(j)+(j_rackVol-currVol);
-                Clength(j) = Clength(j)+(j_rackL-currL);
-                NumRack(j) = NumRack(j)+1;
-                for p = 1:length(stackedMaterInd)
-                    ind(ind==stackedMaterInd(p)) = [];
-                end
-                k = 1;
-                currL = 0;
-                currVol = 0;
-            end
-        end
-    end
-    Objs(i) = sum(C);
-    WasteL(i) = sum(Clength);
-    NumRacks{i} = NumRack;
-end
-end
\ No newline at end of file
diff --git a/matlab/Problems/Real-World/blackstart/adjacent.xlsx b/matlab/Problems/Real-World/blackstart/adjacent.xlsx
deleted file mode 100644
index 49df8e5..0000000
Binary files a/matlab/Problems/Real-World/blackstart/adjacent.xlsx and /dev/null differ
diff --git a/matlab/Problems/Real-World/blackstart/blackstart.m b/matlab/Problems/Real-World/blackstart/blackstart.m
deleted file mode 100644
index f1ab954..0000000
--- a/matlab/Problems/Real-World/blackstart/blackstart.m
+++ /dev/null
@@ -1,454 +0,0 @@
-function [output1,output2,output3] = blackstart(varargin)
-% The black start problem
-
-switch varargin{end}
-    case 'construct'
-        Problem  = varargin{1};
-        instance = varargin{2};
-        % define the bound of decision space
-        a = 2;  % number of BS
-        b = 8;  % number of NBS
-        c = 21; % number of load
-        lower = zeros(1,a*b+b*c); % 1*D, lower bound of the D-dimension decision space
-        upper = ones(1,a*b+b*c);  % 1*D, upper bound of the D-dimension decision space
-        for i = 1:length(instance)
-            Problem(i) = Problem(1);
-            Problem(i).bound = [lower;upper];
-
-            % define problem type in the following three cells (optional), type
-            % can be defined either here or in the Main file
-            % choices:
-            % first cell : 'continuous'\'discrete'\'permutation'
-            % second cell: 'static'\'sequential'
-            % third cell : 'certain'\'uncertain'
-            Problem(i).type = {'discrete','static','certain'};
-        end
-        output1 = Problem;
-
-        % load data file
-        orgData = case3901;
-        Weight  = readmatrix('weight.xlsx','Sheet',1); % the weight of the Loads
-        Data = struct;
-        for k = 1:length(instance)
-            if k == 1
-                BS  = orgData.gen([1,2],:);
-                NBS = orgData.gen(3:10,:);
-            elseif k == 2
-                BS  = orgData.gen([3,7],:);
-                NBS = orgData.gen([1:2,4:6,8:10],:);
-            end
-            Load    = orgData.bus([1,3,4,7,8,9,12,15,16,18,20,21,23:29,31,39],:);
-            branch  = orgData.branch;
-            bus     = orgData.bus;
-
-            % calculate the distance between any two nodes
-            [A,indBS,indNBS,indLoad] = getAdjacent(k);
-            w = zeros(numel(indBS),numel(indNBS));   % for save distance between BS and NBS
-            l = zeros(numel(indNBS),numel(indLoad)); % for save distance between NBS and Load
-            BSNBSPath = cell(numel(indBS),numel(indNBS)); % for save path from BS to NBS
-            NBSLoadPath = cell(numel(indNBS),numel(indLoad)); % for save path from NBS to Load
-            for i = 1:numel(indBS)
-                for j = 1:numel(indNBS)
-                    start = indBS(i);
-                    dest = indNBS(j);
-                    [w(i,j),BSNBSPath{i,j},~] = dijkstra(A,start,dest);
-                end
-            end
-            for i = 1:numel(indNBS)
-                for j = 1:numel(indLoad)
-                    start = indNBS(i);
-                    dest = indLoad(j);
-                    [l(i,j),NBSLoadPath{i,j},~ ] = dijkstra(A,start,dest);
-                end
-            end
-
-            Data(k).BS = BS;
-            Data(k).NBS = NBS;
-            Data(k).Load = Load;
-            Data(k).branch = branch;
-            Data(k).bus = bus;
-            Data(k).w = w;
-            Data(k).l = l;
-            Data(k).BSNBSPath = BSNBSPath;
-            Data(k).NBSLoadPath = NBSLoadPath;
-            Data(k).Weight = Weight;
-        end
-        output2 = Data;
-
-    case 'repair'
-        Data = varargin{1};
-        Decs = varargin{2};
-        a = size(Data.BS,1); % number of BS
-        b = size(Data.NBS,1); % number of NBS
-        c = size(Data.Load,1); % number of load
-        for i = 1:size(Decs,1)
-            v = reshape(Decs(i,1:a*b),b,a);
-            v = v'; % 2*8(a*b)
-            h = reshape(Decs(i,a*b+1:end),c,b);
-            h = h'; % 8*21(b*c)
-            %repair the solution
-            % only one BS to each NBS
-            for g = 1:b % for each NBS
-                if sum(v(:,g)) > 1
-                    BSInd = find(v(:,g)==1);
-                    randInd = randperm(numel(BSInd));
-                    v(BSInd(randInd(1:end-1)),g) = 0;
-                elseif sum(v(:,g)) < 1
-                    randInd = randi(a);
-                    v(randInd,g) = 1;
-                end
-            end
-            % for each load,only one NBS to each load
-            for k = 1:c
-                if sum(h(:,k)) > 1
-                    NBSInd = find(h(:,k)==1);
-                    randInd = randperm(numel(NBSInd));
-                    h(NBSInd(randInd(1:end-1)),k) = 0;
-                elseif sum(h(:,k)) < 1
-                    randInd = randi(b);
-                    h(randInd,k) = 1;
-                end
-            end
-            %give v&h to the Decs(i)
-            f = v';
-            f = reshape(f,1,a*b);
-            e = h';
-            e = reshape(e,1,b*c);
-            Decs(i,1:a*b) = f;
-            Decs(i,a*b+1:end) = e;
-        end
-        output1 = Decs;
-
-    case 'evaluate'
-        Data = varargin{1}; % load problem data
-        Decs = varargin{2}; % load the current solution(s)
-
-        % define the objective function in the following
-        a = size(Data.BS,1); % number of BS
-        b = size(Data.NBS,1); % number of NBS
-        c = size(Data.Load,1); % number of load
-        w = Data.w; % distance between BS and NBS
-        l = Data.l;% distance between NBS and Load
-        Objs = zeros(size(Decs,1),1);
-        for i = 1:size(Decs,1)
-            v = reshape(Decs(i,1:a*b),b,a);
-            v = v'; % 2*8(a*b)
-            h = reshape(Decs(i,a*b+1:end),c,b);
-            h = h'; % 8*21(b*c)
-            F = zeros(b,1);
-            for j = 1:b
-                F(j) = v(:,j)'*w(:,j);
-            end
-            F = sum(F);
-            G = zeros(c,1);
-            for k = 1:c
-                G(k) = h(:,k)'*l(:,k);
-            end
-            G = sum(G);
-            Objs(i) = F+G;
-        end
-
-
-        % calculate the priority of NBS
-        Weight = Data.Weight;
-        maxW = zeros(b,1); % power of the most important load of each NBS
-        for i = 1:size(h,1)
-            indLoadThisNBS = find(h(i,:)==1); % indexes of the loads belong to NBS i
-            indLoadThisNBS = indLoadThisNBS';
-            if isempty(indLoadThisNBS) == 0
-                WeightThisNBS = Weight(indLoadThisNBS,1);
-            else
-                WeightThisNBS = 0;
-            end
-            maxW(i) = max(WeightThisNBS);
-        end
-        [~,priority] = sort(maxW,'descend'); % run NBS (index) one by one according to priority
-
-        % define the inequal constraint(s) in the following, equal
-        % constraints should be transformed to inequal ones
-        restoredbranchInd = {};
-        CV = zeros(size(Decs,1),1);
-
-        for i = 1:size(Decs,1)
-            % extract the path according to solution
-            BSNBSInd = zeros(1,b);  %�ĸ�BS�����ĸ�NBS����Ӧ��λ�ã�
-            BSNBSPath = Data.BSNBSPath;%BS to NBS��·��
-            thisBSNBSPath = cell(b,1);%���ݽ⣬��ȡ��BStoNBS��·��
-            NBSLoadInd = zeros(1,c);%�ĸ�NBS�����ĸ�Load����Ӧ��λ�ã�
-            NBSLoadPath = Data.NBSLoadPath;%NBS to Load��·��
-            thisNBSLoadPath = cell(c,1);%���ݽ⣬��ȡ��NBStoLoad��·��
-            % ����solution����ȡBS��NBS��·��
-            for j = 1:size(v,2)
-                BSNBSInd(j) = find(v(:,j)==1);
-                thisBSNBSPath{j} =  BSNBSPath{BSNBSInd(j),j};
-            end
-            % ����solution����ȡNBS��Load��·��
-            for j = 1:size(h,2)
-                NBSLoadInd(j) = find(h(:,j)==1);
-                thisNBSLoadPath{j} =  NBSLoadPath{NBSLoadInd(j),j};
-            end
-
-            %���¸�����״̬
-            ubranch = zeros(size(Data.branch,1),1);% branch��״̬
-            ust = zeros(b,1); % starting state of NBS
-            uramp = zeros(b,1); % rumping state of NBS
-            %             ubus = zeros(size(Data.bus,1),1);
-            %             ubus(30:31) = 1;
-            %BS�ָ���then��BSֱ��������line�ָ�(��BS�ڵ��һ����line)
-            for j = 1:size(Data.BS,1)
-                thisBSbranchind = Data.branch(:,2)==Data.BS(j,1);%�ҵ���BS�ڵ����ȵ�tbus��λ�ã����λ��Ҳ����branch�ı�ţ�
-                ubranch(thisBSbranchind) = 1;
-            end
-
-            %��line�����߻ָ������ָܻ�(�������й�ͬ�Ľڵ�)
-            %����NBS���ȼ�������BStoNBS��each·��������״̬
-            for m = 1:numel(priority)
-                for k = 1:numel(thisBSNBSPath{priority(m)}(1:end-1))
-                    for j = 1:size(Data.branch,1)
-                        if ubranch(j) == 1
-                            if thisBSNBSPath{priority(m)}(k) == Data.branch(j,1)
-                                connBusInd = k;%��thisBSNBSPath���ҵ���branchj�й�ͬ�ڵ�Ľڵ�ŵ�λ�ã���thisBSNBSPath��priority��m���еĵڼ�����
-                                if thisBSNBSPath{priority(m)}(connBusInd)<thisBSNBSPath{priority(m)}(connBusInd+1)%���ݽڵ�ţ��ҵ�thisBSNBSPath�дνڵ������һ�ڵ����ɵ�branch��ind
-                                    allconnBranchInd = find(Data.branch(:,1)==thisBSNBSPath{priority(m)}(connBusInd));%���ݽڵ�ţ���branch��������ҵ���ýڵ����ͬ������branch�ı��
-                                    connBranchInd = find(Data.branch(allconnBranchInd,2)==thisBSNBSPath{priority(m)}(connBusInd+1));%���ҵ��ı�����branch���յ����ҳ�����һ���ڵ����ͬ��branch�ı��
-                                    %connBranchInd = find(Data.branch(:,1)==thisBSNBSPath{priority(m)}(connBusInd)&&Data.branch(:,2)==thisBSNBSPath{priority(m)}(connBusInd+1));%�ҵ��Ľڵ�ű�����������Ľڵ��С
-                                else
-                                    allconnBranchInd = find(Data.branch(:,1)==thisBSNBSPath{priority(m)}(connBusInd+1));
-                                    connBranchInd = find(Data.branch(allconnBranchInd,2)==thisBSNBSPath{priority(m)}(connBusInd));
-                                    %connBranchInd = find(Data.branch(:,1)==thisBSNBSPath{priority(m)}(connBusInd+1)&&Data.branch(:,2)==thisBSNBSPath{priority(m)}(connBusInd));%�ҵ��Ľڵ�ű�����������Ľڵ�Ŵ�
-                                end
-                                ubranch(allconnBranchInd(connBranchInd))=1;
-                                restoredbranchInd = {restoredbranchInd,allconnBranchInd(connBranchInd)};
-                            end
-                            if thisBSNBSPath{priority(m)}(k) == Data.branch(j,2)
-                                connBusInd = k;%��thisBSNBSPath���ҵ���branchj�й�ͬ�ڵ�Ľڵ�ŵ�λ�ã���thisBSNBSPath��priority��m���еĵڼ�����
-                                if thisBSNBSPath{priority(m)}(connBusInd)<thisBSNBSPath{priority(m)}(connBusInd+1)%���ݽڵ�ţ��ҵ�thisBSNBSPath�дνڵ������һ�ڵ����ɵ�branch��ind
-                                    allconnBranchInd = find(Data.branch(:,1)==thisBSNBSPath{priority(m)}(connBusInd));
-                                    connBranchInd = find(Data.branch(allconnBranchInd,2)==thisBSNBSPath{priority(m)}(connBusInd+1));
-                                    %connBranchInd = find(Data.branch(:,1)==thisBSNBSPath{priority(m)}(connBusInd)&&Data.branch(:,2)==thisBSNBSPath{priority(m)}(connBusInd+1));%�ҵ��Ľڵ�ű�����������Ľڵ��С
-                                else
-                                    allconnBranchInd = find(Data.branch(:,1)==thisBSNBSPath{priority(m)}(connBusInd+1));
-                                    connBranchInd = find(Data.branch(allconnBranchInd,2)==thisBSNBSPath{priority(m)}(connBusInd));
-                                    %connBranchInd = find(Data.branch(:,1)==thisBSNBSPath{priority(m)}(connBusInd+1)&&Data.branch(:,2)==thisBSNBSPath{priority(m)}(connBusInd));%�ҵ��Ľڵ�ű�����������Ľڵ�Ŵ�
-                                end
-                                ubranch(allconnBranchInd(connBranchInd))=1;
-                                restoredbranchInd = {restoredbranchInd,allconnBranchInd(connBranchInd)};
-                            end
-                        end
-                    end
-                end
-            end
-
-            %����NBStoLoad��״̬
-            for m = 1:size(thisNBSLoadPath,1)
-                for k = 1:numel(thisNBSLoadPath{m}(1:end-1))
-                    for j = 1:size(Data.branch,1)
-                        if ubranch(j) == 1
-                            if thisNBSLoadPath{m}(k) == Data.branch(j,1)%·��m�ĵ�k����branchj�����ͬ
-                                connBusInd = k;%��thisNBSLoadPath��ҵ���branchj�������յ���ͬ���Ǹ��ڵ�����ڵ�λ��
-                                if thisNBSLoadPath{m}(connBusInd)<thisNBSLoadPath{m}(connBusInd+1)%����ҵ��Ľڵ����thisNBSLoadPath���Ӧ�Ľڵ�ű���һ��С
-                                    allconnBranchInd = find(Data.branch(:,1)== thisNBSLoadPath{m}(connBusInd));%��branch�У��ҵ����������ýڵ���ͬ��branch���
-                                    connBranchInd = find(Data.branch(allconnBranchInd,2)==thisNBSLoadPath{m}(connBusInd+1));%������branch����У��ҵ��յ���ýڵ���һ���ڵ���ͬ��branch��ţ�Ψһ��
-                                    %connBranchInd = find(Data.branch(:,1)== thisNBSLoadPath{m}(connBusInd)&&Data.branch(:,2)==thisNBSLoadPath{m}(connBusInd+1));%����Ǵ�ind
-                                else
-                                    allconnBranchInd = find(Data.branch(:,1)== thisNBSLoadPath{m}(connBusInd+1));%��branch�У��ҵ����������ýڵ���һ���ڵ���ͬ��branch���
-                                    connBranchInd = find(Data.branch(allconnBranchInd,2)==thisNBSLoadPath{m}(connBusInd));%����������У��ҵ��յ���ýڵ���ͬ��branch��ţ�Ψһ��
-                                    %connBranchInd = find(Data.branch(:,1)== thisNBSLoadPath{m}(connBusInd+1)&&Data.branch(:,2)==thisNBSLoadPath{m}(connBusInd));%����Ǵ�ind����һ���ڵ��
-                                end
-                                ubranch(allconnBranchInd(connBranchInd))=1;
-                            end
-                            if thisNBSLoadPath{m}(k) == Data.branch(j,2)%·��m�ĵ�k����branchj�յ���ͬ
-                                connBusInd = k;%��thisNBSLoadPath��ҵ���branchj�������յ���ͬ���Ǹ��ڵ�����ڵ�λ��
-                                if thisNBSLoadPath{m}(connBusInd)<thisNBSLoadPath{m}(connBusInd+1)%����ҵ��Ľڵ����thisNBSLoadPath���Ӧ�Ľڵ�ű���һ��С
-                                    allconnBranchInd = find(Data.branch(:,1)==thisNBSLoadPath{m}(connBusInd));%��branch�У��ҵ����������ýڵ���ͬ��branch���
-                                    connBranchInd = find(Data.branch(allconnBranchInd,2)==thisNBSLoadPath{m}(connBusInd+1));%������branch����У��ҵ��յ���ýڵ���һ���ڵ���ͬ��branch��ţ�Ψһ��
-                                    %connBranchInd = find(Data.branch(:,1)== thisNBSLoadPath{m}(connBusInd)&&Data.branch(:,2)==thisNBSLoadPath{m}(connBusInd+1));%����Ǵ�ind
-                                else
-                                    allconnBranchInd = find(Data.branch(:,1)==thisNBSLoadPath{m}(connBusInd+1));%��branch�У��ҵ����������ýڵ���һ���ڵ���ͬ��branch���
-                                    connBranchInd = find(Data.branch(allconnBranchInd,2)==thisNBSLoadPath{m}(connBusInd));%����������У��ҵ��յ���ýڵ���ͬ��branch��ţ�Ψһ��
-                                    %connBranchInd = find(Data.branch(:,1)== thisNBSLoadPath{m}(connBusInd+1)&&Data.branch(:,2)==thisNBSLoadPath{m}(connBusInd));%����Ǵ�ind����һ���ڵ��
-                                end
-                                ubranch(allconnBranchInd(connBranchInd))=1;
-                            end
-                        end
-                    end
-                end
-            end
-            %                 for j = 1:size(Data.branch,1)
-            %                     connBranchInd = [];
-            %                     % check which branch is connected with branch j
-            %                     for k = 1:size(Data.branch,1)
-            %                         if Data.branch(j,1) == Data.branch(k,1) || Data.branch(j,1) == Data.branch(k,2) || Data.branch(j,2) == Data.branch(k,1) || Data.branch(j,2) == Data.branch(k,2)
-            %                             connBranchInd = [connBranchInd,k];
-            %                         end
-            %                     end
-            %                     if sum(ubranch(connBranchInd)) >= 1
-            %                         ubranch(j) = 1;
-            %                     end
-            %                 end
-
-            %for each NBS��NBS������line�ָ���thenNBS�ָ�(�й�ͬ�ڵ�)
-            for n = 1:numel(priority)
-                thisNBSbranchind = Data.branch(:,2)==Data.NBS(priority(n),1);%�ҵ���NBS�ڵ����ͬ��tbus��λ�ã���λ���Ƕ�Ӧ��branch�ı�ţ�
-                if sum(ubranch(thisNBSbranchind)) >= 1
-                    ust(priority(n)) = 1;
-                else
-                    ust(priority(n)) = 0;
-                end
-
-                %update the ramping state of the NBS
-                P_ramp = Data.NBS(:,18);%���������ҵ�P_ramp
-                P = Data.NBS(:,2);
-                if P(priority(n)) > P_ramp(priority(n))
-                    uramp(priority(n)) = 1;
-                else
-                    uramp(priority(n)) = 0;
-                end
-            end
-
-            orgCase = case3901;
-            updatedCase = orgCase;
-            updatedCase.branch(:,11) = ubranch;
-            updatedCase.gen(3:end,8) = ust;
-            save('newcase.mat',"updatedCase");
-            %results = runpf('newcase');
-            results = runpf(updatedCase);
-            if results.iterations == 10
-                results.success
-            end
-            if results.success == 0
-                CV(i) = 10^6;
-                continue
-            end
-
-
-
-
-            %������������Լ��������Լ��
-            %��results����ȡ����
-            P = results.gen(:,2);%����
-            Q = results.gen(:,3);%�޹����
-            Pmax = results.gen(:,9);%����������
-            %             P_ramp = results.gen(:,18);%���¹���
-            Qmax = results.gen(:,4);%�������޹�
-            V = results.bus(:,8);%��ѹ��ֵ
-            Vmax = results.bus(:,12);%����ѹ
-            Vmin = results.bus(:,13);%��С��ѹ
-            %BS+NBS�������Լ��
-            p1 = zeros(a+b,1);
-            for g = 1:a+b
-                if Pmax(g)-P(g)>=0 && P(g)>=0 && Qmax(g)-Q(g)>=0 && Q(g)>=0
-                    P(g) = P(g);
-                else
-                    p1(g) = abs(Pmax(g)-P(g))+abs(P(g));
-                end
-                p1 = sum(p1);
-            end
-            %NBS����Լ��
-            %             for g = 1:b
-            %                 if (P_ramp(g)/10)*T-(P(g,t)-P(g,t-1)) >= 0&&P(g,t)-P(g,t-1) >= 0
-            %                     P(g,t) = P(g,t);
-            %                 else
-            %                     p2 = abs((P_ramp(g)/10)*T-(P(g,t)-P(g,t-1)))+abs(P(g,t)-P(g,t-1));
-            %                 end
-            %             end
-
-            %����Լ���������·for each branch��
-            Smax = Data.branch(:,6);
-            PF = results.branch(:,14);
-            QF = results.branch(:,15);
-            p3 = zeros(46,1);
-            for j = 1:size(Data.branch,1)
-                if Smax(j)^2*ubranch(j)-(PF(j)^2+QF(j)^2) < 0
-                    p3(j) = abs(Smax(j)^2*ubranch(j)-PF(j)^2-QF(j)^2);
-                end
-                p3 = sum(p3);
-            end
-            %��ѹԼ��
-            p4 = zeros(39,1);
-            for d = 1:size(Data.bus,1)
-                if Vmax(d)-V(d)<0 || V(d)-Vmin(d)<0
-                    p4(d) = abs(Vmax(d)-V(d))+abs(V(d)-Vmin(d));
-                end
-                p4 = sum(p4);
-            end
-
-            %��ʽԼ��
-            %����ƽ��Լ��
-            PD = Data.bus(:,3);
-            QD = Data.bus(:,4);
-            PT = results.branch(:,16);
-            QT = results.branch(:,17);
-            p2 = zeros(29,1);
-            p5 = zeros(29,1);
-            for j = 1:29
-                if PD(j)>0
-                    connbranch1Ind = find(Data.branch(:,1)==j);
-                    connbranch2Ind = find(Data.branch(:,2)==j);
-                    if PD(j)+sum(PF(connbranch1Ind))+sum(PT(connbranch2Ind)) ~= 0
-                        p2(j) = abs(PD(j))+abs(sum(PF(connbranch1Ind)))+abs(sum(PF(connbranch2Ind)));
-                    end
-                    if QD(j)+sum(QF(connbranch1Ind))+sum(QT(connbranch2Ind)) ~= 0
-                        p5(j) = abs(QD(j))+abs(sum(QF(connbranch1Ind)))+abs(sum(QT(connbranch2Ind)));
-                    end
-                end
-                if PD(j) == 0
-                    connbranch1Ind = find(Data.branch(:,1)==j);
-                    connbranch2Ind = find(Data.branch(:,2)==j);
-                    if sum(PF(connbranch1Ind))+sum(PT(connbranch2Ind)) ~= 0
-                        p2(j) = abs(sum(PF(connbranch1Ind)))+abs(sum(PF(connbranch2Ind)));
-                    end
-                    if sum(QF(connbranch1Ind))+sum(QT(connbranch2Ind)) ~= 0
-                        p5(j) = abs(sum(QF(connbranch1Ind)))+abs(sum(QT(connbranch2Ind)));
-                    end
-                end
-                p2 = sum(p2);
-                p5 = sum(p5);
-            end
-
-            %BS����NBS�Ĺ���Լ��
-            Pramp = Data.NBS(:,18);
-            PBS = Data.BS(:,17);
-            p6 = zeros(size(Data.BS,1),1);
-            for j = 1:size(Data.BS,1)
-                BSNInd = find(v(j,:)==1);
-                BSNInd = BSNInd';
-                if sum(Pramp(BSNInd)) > PBS(j)
-                    p6(j) = sum(Pramp(BSNInd)) - PBS(j);
-                end
-                p6 = sum(p6);
-            end
-
-            %NBS����Load�Ĺ���Լ��
-            Pout = results.gen([1:2,4:6,8:10],2);
-            PL = results.bus([1,3,4,7,8,9,12,15,16,18,20,21,23:29,31,39],3);
-            p7 = zeros(size(Data.NBS,1),1);
-            for j = 1:size(Data.NBS,1)
-                NBSLInd = find(h(j,:)==1);
-                NBSLInd = NBSLInd';
-                if sum(PL(NBSLInd)) > Pout(j)
-                    p7(j) = sum(PL(NBSLInd))-Pout(j);
-                end
-                p7 = sum(p7);
-            end
-
-            % calculate the constraint violation in the following
-            CV(i) = p1+p2+p3+p4+p5+p6+p7;
-        end
-
-        % collect accessory data for understanding the solutions in the
-        % following (optional)
-
-        output1 = Objs; % matrix for saving objective function values
-        output2 = CV; % matrix for saving constraint violation values (optional)
-        output3 = []; % matrix or cells for saving accessory data (optional), a solution's accessory data should be saved in a row
-        
-        %output1 = output1 + output2
-end
-
-if ~exist('output2','var')
-    output2 = [];
-elseif ~exist('output3','var')
-    output3 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Problems/Real-World/blackstart/case3901.m b/matlab/Problems/Real-World/blackstart/case3901.m
deleted file mode 100644
index ab17615..0000000
--- a/matlab/Problems/Real-World/blackstart/case3901.m
+++ /dev/null
@@ -1,205 +0,0 @@
-function mpc = case3901
-%CASE39 Power flow data for 39 bus New England system.
-%   Please see CASEFORMAT for details on the case file format.
-%
-%   Data taken from [1] with the following modifications/additions:
-%
-%       - renumbered gen buses consecutively (as in [2] and [4])
-%       - added Pmin = 0 for all gens
-%       - added Qmin, Qmax for gens at 31 & 39 (copied from gen at 35)
-%       - added Vg based on V in bus data (missing for bus 39)
-%       - added Vg, Pg, Pd, Qd at bus 39 from [2] (same in [4])
-%       - added Pmax at bus 39: Pmax = Pg + 100
-%       - added line flow limits and area data from [4]
-%       - added voltage limits, Vmax = 1.06, Vmin = 0.94
-%       - added identical quadratic generator costs
-%       - increased Pmax for gen at bus 34 from 308 to 508
-%         (assumed typo in [1], makes initial solved case feasible)
-%       - re-solved power flow
-% 
-%   Notes:
-%       - Bus 39, its generator and 2 connecting lines were added
-%         (by authors of [1]) to represent the interconnection with
-%         the rest of the eastern interconnect, and did not include
-%         Vg, Pg, Qg, Pd, Qd, Pmin, Pmax, Qmin or Qmax.
-%       - As the swing bus, bus 31 did not include and Q limits.
-%       - The voltages, etc in [1] appear to be quite close to the
-%         power flow solution of the case before adding bus 39 with
-%         it's generator and connecting branches, though the solution
-%         is not exact.
-%       - Explicit voltage setpoints for gen buses are not given, so
-%         they are taken from the bus data, however this results in two
-%         binding Q limits at buses 34 & 37, so the corresponding
-%         voltages have probably deviated from their original setpoints.
-%       - The generator locations and types are as follows:
-%           1   30      hydro
-%           2   31      nuke01
-%           3   32      nuke02
-%           4   33      fossil02
-%           5   34      fossil01
-%           6   35      nuke03
-%           7   36      fossil04
-%           8   37      nuke04
-%           9   38      nuke05
-%           10  39      interconnection to rest of US/Canada
-%
-%   This is a solved power flow case, but it includes the following
-%   violations:
-%       - Pmax violated at bus 31: Pg = 677.87, Pmax = 646
-%       - Qmin violated at bus 37: Qg = -1.37,  Qmin = 0
-%
-%   References:
-%   [1] G. W. Bills, et.al., "On-Line Stability Analysis Study"
-%       RP90-1 Report for the Edison Electric Institute, October 12, 1970,
-%       pp. 1-20 - 1-35.
-%       prepared by E. M. Gulachenski - New England Electric System
-%                   J. M. Undrill     - General Electric Co.
-%       "generally representative of the New England 345 KV system, but is
-%        not an exact or complete model of any past, present or projected
-%        configuration of the actual New England 345 KV system.
-%   [2] M. A. Pai, Energy Function Analysis for Power System Stability,
-%       Kluwer Academic Publishers, Boston, 1989.
-%       (references [3] as source of data)
-%   [3] Athay, T.; Podmore, R.; Virmani, S., "A Practical Method for the
-%       Direct Analysis of Transient Stability," IEEE Transactions on Power
-%       Apparatus and Systems , vol.PAS-98, no.2, pp.573-584, March 1979.
-%       URL: https://doi.org/10.1109/TPAS.1979.319407
-%       (references [1] as source of data)
-%   [4] Data included with TC Calculator at http://www.pserc.cornell.edu/tcc/
-%       for 39-bus system.
-
-%   MATPOWER
-
-%% MATPOWER Case Format : Version 2
-mpc.version = '2';
-
-%%-----  Power Flow Data  -----%%
-%% system MVA base
-mpc.baseMVA = 100;
-
-%% bus data
-%	bus_i	type	Pd	Qd	Gs	Bs	area	Vm	Va	baseKV	zone	Vmax	Vmin
-mpc.bus = [
-	1	1	97.6	44.2	0	0	2	1.0393836	-13.536602	345	1	1.06	0.94;
-	2	1	0	0	0	0	2	1.0484941	-9.7852666	345	1	1.06	0.94;
-	3	1	322	2.4	0	0	2	1.0307077	-12.276384	345	1	1.06	0.94;
-	4	1	500	184	0	0	1	1.00446	-12.626734	345	1	1.06	0.94;
-	5	1	0	0	0	0	1	1.0060063	-11.192339	345	1	1.06	0.94;
-	6	1	0	0	0	0	1	1.0082256	-10.40833	345	1	1.06	0.94;
-	7	1	233.8	84	0	0	1	0.99839728	-12.755626	345	1	1.06	0.94;
-	8	1	522	176.6	0	0	1	0.99787232	-13.335844	345	1	1.06	0.94;
-	9	1	6.5	-66.6	0	0	1	1.038332	-14.178442	345	1	1.06	0.94;
-	10	1	0	0	0	0	1	1.0178431	-8.170875	345	1	1.06	0.94;
-	11	1	0	0	0	0	1	1.0133858	-8.9369663	345	1	1.06	0.94;
-	12	1	8.53	88	0	0	1	1.000815	-8.9988236	345	1	1.06	0.94;
-	13	1	0	0	0	0	1	1.014923	-8.9299272	345	1	1.06	0.94;
-	14	1	0	0	0	0	1	1.012319	-10.715295	345	1	1.06	0.94;
-	15	1	320	153	0	0	3	1.0161854	-11.345399	345	1	1.06	0.94;
-	16	1	329	32.3	0	0	3	1.0325203	-10.033348	345	1	1.06	0.94;
-	17	1	0	0	0	0	2	1.0342365	-11.116436	345	1	1.06	0.94;
-	18	1	158	30	0	0	2	1.0315726	-11.986168	345	1	1.06	0.94;
-	19	1	0	0	0	0	3	1.0501068	-5.4100729	345	1	1.06	0.94;
-	20	1	680	103	0	0	3	0.99101054	-6.8211783	345	1	1.06	0.94;
-	21	1	274	115	0	0	3	1.0323192	-7.6287461	345	1	1.06	0.94;
-	22	1	0	0	0	0	3	1.0501427	-3.1831199	345	1	1.06	0.94;
-	23	1	247.5	84.6	0	0	3	1.0451451	-3.3812763	345	1	1.06	0.94;
-	24	1	308.6	-92.2	0	0	3	1.038001	-9.9137585	345	1	1.06	0.94;
-	25	1	224	47.2	0	0	2	1.0576827	-8.3692354	345	1	1.06	0.94;
-	26	1	139	17	0	0	2	1.0525613	-9.4387696	345	1	1.06	0.94;
-	27	1	281	75.5	0	0	2	1.0383449	-11.362152	345	1	1.06	0.94;
-	28	1	206	27.6	0	0	3	1.0503737	-5.9283592	345	1	1.06	0.94;
-	29	1	283.5	26.9	0	0	3	1.0501149	-3.1698741	345	1	1.06	0.94;
-	30	2	0	0	0	0	2	1.0499	-7.3704746	345	1	1.06	0.94;
-	31	3	9.2	4.6	0	0	1	0.982	0	345	1	1.06	0.94;
-	32	2	0	0	0	0	1	0.9841	-0.1884374	345	1	1.06	0.94;
-	33	2	0	0	0	0	3	0.9972	-0.19317445	345	1	1.06	0.94;
-	34	2	0	0	0	0	3	1.0123	-1.631119	345	1	1.06	0.94;
-	35	2	0	0	0	0	3	1.0494	1.7765069	345	1	1.06	0.94;
-	36	2	0	0	0	0	3	1.0636	4.4684374	345	1	1.06	0.94;
-	37	2	0	0	0	0	2	1.0275	-1.5828988	345	1	1.06	0.94;
-	38	2	0	0	0	0	3	1.0265	3.8928177	345	1	1.06	0.94;
-	39	2	1104	250	0	0	1	1.03	-14.535256	345	1	1.06	0.94;
-];
-
-%% generator data
-%	bus	Pg	Qg	Qmax	Qmin	Vg	mBase	ust	Pmax	Pmin	Pc1	Pc2	Qc1min	Qc1max	Qc2min	Qc2max	ramp_agc	Pstart	ramp_30	ramp_q	apf
-mpc.gen = [
-	30	250	161.762	400	140	1.0499	100	1	1140	0	0	0	0	0	0	0	500	0	0	0	0;
-	31	677.871	221.574	300	-100	0.982	100	1	700	0	0	0	0	0	0	0	500	0	0	0	0;
-	32	650	206.965	300	150	0.9841	100	0	725	0	0	0	0	0	0	0	0	110	0	0	0;
-	33	632	108.293	250	0	0.9972	100	0	652	0	0	0	0	0	0	0	0	110	0	0	0;
-	34	508	166.688	167	0	1.0123	100	0	508	0	0	0	0	0	0	0	0	110	0	0	0;
-	35	650	210.661	300	-100	1.0494	100	0	687	0	0	0	0	0	0	0	0	110	0	0	0;
-	36	560	100.165	240	0	1.0636	100	0	580	0	0	0	0	0	0	0	0	110	0	0	0;
-	37	540	-1.36945	250	0	1.0275	100	0	564	0	0	0	0	0	0	0	0	110	0	0	0;
-	38	830	21.7327	300	-150	1.0265	100	0	865	0	0	0	0	0	0	0	0	110	0	0	0;
-	39	1000	78.4674	300	-100	1.03	100	0	1100	0	0	0	0	0	0	0	0	110	0	0	0;
-];
-
-%% branch data
-%	fbus	tbus	r	x	b	rateA	rateB	rateC	ratio	angle	ubranch	angmin	angmax
-mpc.branch = [
-	1	2	0.0035	0.0411	0.6987	600	600	600	0	0	0	-360	360;
-	1	39	0.001	0.025	0.75	1000	1000	1000	0	0	0	-360	360;
-	2	3	0.0013	0.0151	0.2572	500	500	500	0	0	0	-360	360;
-	2	25	0.007	0.0086	0.146	500	500	500	0	0	0	-360	360;
-	2	30	0	0.0181	0	900	900	2500	1.025	0	0	-360	360;
-	3	4	0.0013	0.0213	0.2214	500	500	500	0	0	0	-360	360;
-	3	18	0.0011	0.0133	0.2138	500	500	500	0	0	0	-360	360;
-	4	5	0.0008	0.0128	0.1342	600	600	600	0	0	0	-360	360;
-	4	14	0.0008	0.0129	0.1382	500	500	500	0	0	0	-360	360;
-	5	6	0.0002	0.0026	0.0434	1200	1200	1200	0	0	0	-360	360;
-	5	8	0.0008	0.0112	0.1476	900	900	900	0	0	0	-360	360;
-	6	7	0.0006	0.0092	0.113	900	900	900	0	0	0	-360	360;
-	6	11	0.0007	0.0082	0.1389	480	480	480	0	0	0	-360	360;
-	6	31	0	0.025	0	1800	1800	1800	1.07	0	0	-360	360;
-	7	8	0.0004	0.0046	0.078	900	900	900	0	0	0	-360	360;
-	8	9	0.0023	0.0363	0.3804	900	900	900	0	0	0	-360	360;
-	9	39	0.001	0.025	1.2	900	900	900	0	0	0	-360	360;
-	10	11	0.0004	0.0043	0.0729	600	600	600	0	0	0	-360	360;
-	10	13	0.0004	0.0043	0.0729	600	600	600	0	0	0	-360	360;
-	10	32	0	0.02	0	900	900	2500	1.07	0	0	-360	360;
-	12	11	0.0016	0.0435	0	500	500	500	1.006	0	0	-360	360;
-	12	13	0.0016	0.0435	0	500	500	500	1.006	0	0	-360	360;
-	13	14	0.0009	0.0101	0.1723	600	600	600	0	0	0	-360	360;
-	14	15	0.0018	0.0217	0.366	600	600	600	0	0	0	-360	360;
-	15	16	0.0009	0.0094	0.171	600	600	600	0	0	0	-360	360;
-	16	17	0.0007	0.0089	0.1342	600	600	600	0	0	0	-360	360;
-	16	19	0.0016	0.0195	0.304	600	600	2500	0	0	0	-360	360;
-	16	21	0.0008	0.0135	0.2548	600	600	600	0	0	0	-360	360;
-	16	24	0.0003	0.0059	0.068	600	600	600	0	0	0	-360	360;
-	17	18	0.0007	0.0082	0.1319	600	600	600	0	0	0	-360	360;
-	17	27	0.0013	0.0173	0.3216	600	600	600	0	0	0	-360	360;
-	19	20	0.0007	0.0138	0	900	900	2500	1.06	0	0	-360	360;
-	19	33	0.0007	0.0142	0	900	900	2500	1.07	0	0	-360	360;
-	20	34	0.0009	0.018	0	900	900	2500	1.009	0	0	-360	360;
-	21	22	0.0008	0.014	0.2565	900	900	900	0	0	0	-360	360;
-	22	23	0.0006	0.0096	0.1846	600	600	600	0	0	0	-360	360;
-	22	35	0	0.0143	0	900	900	2500	1.025	0	0	-360	360;
-	23	24	0.0022	0.035	0.361	600	600	600	0	0	0	-360	360;
-	23	36	0.0005	0.0272	0	900	900	2500	1	0	0	-360	360;
-	25	26	0.0032	0.0323	0.531	600	600	600	0	0	0	-360	360;
-	25	37	0.0006	0.0232	0	900	900	2500	1.025	0	0	-360	360;
-	26	27	0.0014	0.0147	0.2396	600	600	600	0	0	0	-360	360;
-	26	28	0.0043	0.0474	0.7802	600	600	600	0	0	0	-360	360;
-	26	29	0.0057	0.0625	1.029	600	600	600	0	0	0	-360	360;
-	28	29	0.0014	0.0151	0.249	600	600	600	0	0	0	-360	360;
-	29	38	0.0008	0.0156	0	1200	1200	2500	1.025	0	0	-360	360;
-];
-
-%%-----  OPF Data  -----%%
-%% generator cost data
-%	1	startup	shutdown	n	x1	y1	...	xn	yn
-%	2	startup	shutdown	n	c(n-1)	...	c0
-mpc.gencost = [
-	2	0	0	3	0.01	0.3	0.2;
-	2	0	0	3	0.01	0.3	0.2;
-	2	0	0	3	0.01	0.3	0.2;
-	2	0	0	3	0.01	0.3	0.2;
-	2	0	0	3	0.01	0.3	0.2;
-	2	0	0	3	0.01	0.3	0.2;
-	2	0	0	3	0.01	0.3	0.2;
-	2	0	0	3	0.01	0.3	0.2;
-	2	0	0	3	0.01	0.3	0.2;
-	2	0	0	3	0.01	0.3	0.2;
-];
diff --git a/matlab/Problems/Real-World/blackstart/dijkstra.m b/matlab/Problems/Real-World/blackstart/dijkstra.m
deleted file mode 100644
index bb78cb2..0000000
--- a/matlab/Problems/Real-World/blackstart/dijkstra.m
+++ /dev/null
@@ -1,67 +0,0 @@
-% �ļ�����dijkstra.m
-% ʱ�䣺2020��9��12��
-% ��Դ��https://blog.csdn.net/lishan132/article/details/108527271
-% ���ܣ�����dijkstra�㷨�������������·��
-% dist��������յ�֮�����̾���ֵ
-% path�����·������
-% Distance�����·���µľ���ֵ
-% A���ڽӾ���
-% strat�������
-% dest���յ���
-function [dist,resultPath,Distance] = dijkstra(A,start,dest)
-% �������� 
-% �����������ʱ��
-% tic  %��ʼ��ʱ
-
-% ��ʼ������
-p = size(A,1);        %���㶥����Ŀ 
-S(1) = dest;          %��ʼ������S���Ѽ��뵽·���еĶ�����
-U = 1:p;              %��ʼ������U��δ���뵽·���еĶ�����
-U(dest) = [];         %ɾ���յ���
-Distance = zeros(2,p);  %��ʼ�����ж��㵽�յ�dest�ľ���
-Distance(1,:) = 1:p;    %�ظ�ֵ��һ��Ϊ��������
-Distance(2,1:p) = A(dest,1:p);  %�ظ�ֵ�ڶ���Ϊ�ڽӾ����и����㵽�յ�ľ���
-new_Distance = Distance;
-D = Distance;            %��ʼ��U�����ж��㵽�յ�dest�ľ���
-D(:,dest) = [];          %ɾ��U���յ��ŵ��յ��ŵľ���
-path = zeros(2,p);  %��ʼ��·��
-path(1,:) = 1:p;    %�ظ�ֵ��һ��Ϊ��������
-path(2,Distance(2,:)~=inf) = dest;  %����ֵ��Ϊ�����ʱ��������������
- 
-% Ѱ�����·��
-while ~isempty(U)  %�ж�U��Ԫ���Ƿ�Ϊ��
-    index = find(D(2,:)==min(D(2,:)),1);  %ʣ�ඥ���о�����Сֵ������
-    k = D(1,index);   %����ʣ�ඥ���о����յ�����Ķ�����
-    
-    %���¶���
-    S = [S,k];     %������k���ӵ�S��
-    U(U==k) = [];  %��U��ɾ������k  
-    
-    %�������
-    new_Distance(2,:) = A(k,1:p)+Distance(2,k); %������ͨ�����k���ٴ�k�����յ�����е����ֵ
-    D = min(Distance,new_Distance);  %��ԭ���ľ���ֵ�Ƚϣ�ȡ��Сֵ  
-   
-    %����·��
-    path(2,D(2,:)~=Distance(2,:)) = k;  %�����µ���Сֵ���������ӹ�ϵ�����ӵ����k�� 
-    
-    %���¾���
-    Distance = D;  %���¾����Ϊ���е㵽�յ����Сֵ
-    D(:,S) = [];   %ɾ���Ѽ��뵽S�еĶ���
-end
-dist = Distance(2,start);  %ȡ��ָ����㵽�յ�ľ���ֵ
-% toc %��ʱ����
- 
-% ������
-% fprintf('�ҵ������·��Ϊ��');
-resultPath = [];
-while start ~= dest    %�����յ�ʱ����
-%     fprintf('%d-->',start);  %��ӡ��ǰ����
-    resultPath = [resultPath,start];
-    next = path(2,start);    %�뵱ǰ����������һ����
-    start = next;            %���µ�ǰ��
-end
-resultPath = [resultPath,start];
-% fprintf('%d\n',dest);
-% fprintf('���·����Ӧ�ľ���Ϊ��%d\n',dist);
-end
- 
\ No newline at end of file
diff --git a/matlab/Problems/Real-World/blackstart/getAdjacent.m b/matlab/Problems/Real-World/blackstart/getAdjacent.m
deleted file mode 100644
index 99b7864..0000000
--- a/matlab/Problems/Real-World/blackstart/getAdjacent.m
+++ /dev/null
@@ -1,63 +0,0 @@
-function [A,indBS,indNBS,indLoad] = getAdjacent(k)
-%function [A,node] = getAdjacent
-A = readmatrix('adjacent.xlsx','Sheet',1);
-A(1,:) = []; % delete indexes
-A(:,1) = []; % delete indexes
-for i = 1:size(A,1)
-    for j = 1:size(A,2)
-        if ~isnan(A(i,j))
-            A(j,i) = A(i,j);
-        else
-            A(i,j) = inf; % replace Nan with Inf
-        end
-    end
-end
-
-% A = [0,275.5,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,167.6;%1
-%     275.5,0,101.2,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,57.6,inf,inf,inf,10,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf;%2
-%     inf,101.2,0,142.8,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,89.1,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf;%3
-%     inf,inf,142.8,0,85.8,inf,inf,inf,inf,inf,inf,inf,inf,86.5,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf;%4
-%     inf,inf,inf,85.8,0,17.4,inf,75.1,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf;%5
-%     inf,inf,inf,inf,17.4,0,61.7,inf,inf,inf,55,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,10,inf,inf,inf,inf,inf,inf,inf,inf;%6
-%     inf,inf,inf,inf,inf,61.7,0,30.8,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf;%7
-%     inf,inf,inf,inf,75.1,inf,30.8,0,243.3,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf;%8
-%     inf,inf,inf,inf,inf,inf,inf,243.3,0,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,167.6;%9
-%     inf,inf,inf,inf,inf,inf,inf,inf,inf,0,28.8,inf,28.8,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,10,inf,inf,inf,inf,inf,inf,inf;%10
-%     inf,inf,inf,inf,inf,55.0,inf,inf,inf,28.8,0,10,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf;%11
-%     inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,10,0,10,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf;%12
-%     inf,inf,inf,inf,inf,inf,inf,inf,inf,28.8,inf,10,0,67.7,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf;%13
-%     inf,inf,inf,86.5,inf,inf,inf,inf,inf,inf,inf,inf,67.7,0,145.4,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf;%14
-%     inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,145.4,0,63,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf;%15
-%     inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,63,0,59.7,inf,130.7,inf,90.5,inf,inf,39.5,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf;%16
-%     inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,59.7,0,55,inf,inf,inf,inf,inf,inf,inf,inf,116,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf;%17
-%     inf,inf,89.1,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,55,0,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf;%18
-%     inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,130.7,inf,inf,0,10,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,10,inf,inf,inf,inf,inf,inf;%19
-%     inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,10,0,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,10,inf,inf,inf,inf,inf;%20
-%     inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,90.5,inf,inf,inf,inf,0,93.8,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf;%21
-%     inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,93.8,0,64.3,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,10,inf,inf,inf,inf;%22
-%     inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,64.3,0,234.6,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,10,inf,inf,inf;%23
-%     inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,39.5,inf,inf,inf,inf,inf,inf,234.6,0,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf;%24
-%     inf,57.6,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,0,216.5,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,10,inf,inf;%25
-%     inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,216.5,0,98.5,317.7,418.9,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf;%26
-%     inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,116,inf,inf,inf,inf,inf,inf,inf,inf,98.5,0,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf;%27
-%     inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,317.7,inf,0,101.2,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf;%28
-%     inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,418.9,inf,101.2,0,inf,inf,inf,inf,inf,inf,inf,inf,10,inf;%29
-%     inf,10,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,0,inf,inf,inf,inf,inf,inf,inf,inf,inf;%30
-%     inf,inf,inf,inf,inf,10,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,0,inf,inf,inf,inf,inf,inf,inf,inf;%31
-%     inf,inf,inf,inf,inf,inf,inf,inf,inf,10,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,0,inf,inf,inf,inf,inf,inf,inf;%32
-%     inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,10,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,0,inf,inf,inf,inf,inf,inf;%33
-%     inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,10,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,0,inf,inf,inf,inf,inf;%34
-%     inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,10,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,0,inf,inf,inf,inf;%35
-%     inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,10,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,0,inf,inf,inf;%36
-%     inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,10,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,0,inf,inf;%37
-%     inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,10,inf,inf,inf,inf,inf,inf,inf,inf,0,inf;%38
-%     167.6,inf,inf,inf,inf,inf,inf,inf,167.6,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,inf,0];%39
-if k == 1 % instance 1
-    indBS = [30,31]; 
-    indNBS = 32:39;
-elseif k == 2
-    indBS = [32,36];
-    indNBS = [30:31,33:35,37:39];
-end
-indLoad = [1,3,4,7,8,9,12,15,16,18,20,21,23:29,31,39];
-end
\ No newline at end of file
diff --git a/matlab/Problems/Real-World/blackstart/weight.xlsx b/matlab/Problems/Real-World/blackstart/weight.xlsx
deleted file mode 100644
index 2416dec..0000000
Binary files a/matlab/Problems/Real-World/blackstart/weight.xlsx and /dev/null differ
diff --git a/matlab/Problems/Real-World/blackstart/~$adjacent.xlsx b/matlab/Problems/Real-World/blackstart/~$adjacent.xlsx
deleted file mode 100644
index 0fe2f8c..0000000
Binary files a/matlab/Problems/Real-World/blackstart/~$adjacent.xlsx and /dev/null differ
diff --git a/matlab/Problems/prob_name.m b/matlab/Problems/prob_name.m
deleted file mode 100644
index 8b8d1e3..0000000
--- a/matlab/Problems/prob_name.m
+++ /dev/null
@@ -1,83 +0,0 @@
-function [output1,output2,output3] = prob_name(varargin)
-% Template for writing problem file. 
-
-%----------------------------Copyright-------------------------------------
-% Copyright (C) <2023>  <Qi Zhao>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-switch varargin{end}
-    case 'construct' % define problem properties
-        Problem = varargin{1};
-        % define problem type in the following three cells.
-        % first cell : 'continuous'\'discrete'\'permutation'
-        % second cell: 'static'\'sequential'
-        % third cell : 'certain'\'uncertain'
-        Problem.type = {'','',''}; 
-        
-        % define the bound of solution space
-        lower = []; % 1*D, lower bound of the D-dimension decision space
-        upper = []; % 1*D, upper bound of the D-dimension decision space
-        Problem.bound = [lower;upper];        
-        
-        % define specific settings (optional), options: 
-        % 'dec_diff'         : elements of the solution should be different w.r.t each other for discrete problems
-        % 'uncertain_average': averaging the fitness over multiple fitness evaluations for uncertain problems
-        % 'uncertain_worst'  : use the worse fitness among multiple fitness evaluations as the fitness for uncertain problems
-        Problem.setting = {''}; % put choice(s) into the cell        
-        
-        % set the number of samples for uncertain problems (optional)
-        Problem.sampleN = [];
-               
-        output1 = Problem;
-        
-        % load/construct data file in the following
-        Data = load(''); % for .mat format
-%         Data = readmatrix('','Sheet',1); % for .xlsx format
-        output2 = Data;
-
-    case 'repair' % repair solutions
-        Data = varargin{1};
-        Decs = varargin{2};
-        % define methods for repairing solutions in the following
-       
-        output1 = Decs;
-    
-    case 'evaluate' % evaluate solution's fitness
-        Data = varargin{1}; % load problem data
-        Decs = varargin{2}; % load the current solution(s)
-        
-        % define the objective function in the following
-        
-        % define the inequal constraint(s) in the following, equal constraints should be transformed to inequal ones
-       
-        % calculate the constraint violation in the following
-
-        % collect accessory data for understanding the solutions in the following (optional)
-
-         output1 = ; % matrix for saving objective function values
-         output2 = ; % matrix for saving constraint violation values (optional)
-         output3 = ; % matrix or cells for saving accessory data (optional), a solution's accessory data should be saved in a row
-end
-
-if ~exist('output2','var')
-    output2 = [];
-end
-if ~exist('output3','var')
-    output3 = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Utilities/@DESIGN/DESIGN.m b/matlab/Utilities/@DESIGN/DESIGN.m
deleted file mode 100644
index 6696479..0000000
--- a/matlab/Utilities/@DESIGN/DESIGN.m
+++ /dev/null
@@ -1,170 +0,0 @@
-% Class for designing algorithms
-
-%----------------------------Copyright-------------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-classdef DESIGN < handle
-    properties (SetAccess = private)
-        operator;
-        parameter;
-        operatorPheno;
-        parameterPheno;
-        performance;
-        performanceApprox;
-    end
-
-    methods
-        %% initialize the designed algorithms
-        function obj = DESIGN(varargin)
-            if nargin > 0
-                Problem = varargin{1};
-                Setting = varargin{2};
-                if nargin == 3
-                    N = varargin{3};
-                else
-                    N = Setting.AlgN;
-                end
-                obj(1,N) = DESIGN;
-                [Operators,Paras] = obj.Initialize(Setting,N);
-                [Operators,Paras] = obj.Repair(Operators,Paras,Problem,Setting);
-                for i = 1:N
-                    obj(i).operator  = Operators(i,:);
-                    obj(i).parameter = Paras{i};
-                    [currOp,currPara]     = obj.Decode(Operators(i,:),Paras{i},Problem,Setting);
-                    obj(i).operatorPheno  = currOp;
-                    obj(i).parameterPheno = currPara;
-                    obj(i).performance       = zeros(length(Problem),Setting.AlgRuns);
-                    obj(i).performanceApprox = zeros(length(Problem),Setting.AlgRuns);
-                end
-            end
-        end
-
-        %% design new algorithms based on the current ones
-        function [objNew,Aux] = GetNew(obj,Problem,Setting,innerG,Aux)
-            [NewOp,NewPara,Aux] = obj.Disturb(Setting,innerG,Aux);
-            [Operators,Paras]   = obj.Repair(NewOp,NewPara,Problem,Setting);
-            objNew(1,Setting.AlgN) = DESIGN;
-            for i = 1:Setting.AlgN
-                objNew(i).operator  = Operators(i,:);
-                objNew(i).parameter = Paras{i};
-                [currOp,currPara]     = objNew.Decode(Operators(i,:),Paras{i},Problem,Setting);
-                objNew(i).operatorPheno  = currOp;
-                objNew(i).parameterPheno = currPara;
-                objNew(i).performance       = zeros(length(Problem),Setting.AlgRuns);
-                objNew(i).performanceApprox = zeros(length(Problem),Setting.AlgRuns);
-            end
-        end
-
-        %% get algorithms' average performance or statistically comparing results
-        function value = GetPerformance(obj,Setting,seedInstance)
-            allPerform = zeros(numel(seedInstance)*Setting.AlgRuns,length(obj));
-            for i = 1:length(obj)
-                % reshape algorithm i's all performance values (each run on each instance) to a column vector
-                if strcmp(Setting.Evaluate,'approximate') && sum(obj(i).performanceApprox,'all') ~= 0 && sum(obj(i).performance,'all') == 0
-                    allPerform(:,i) = reshape(obj(i).performanceApprox(seedInstance,:)',size(allPerform,1),1);
-                else
-                    allPerform(:,i) = reshape(obj(i).performance(seedInstance,:)',size(allPerform,1),1);
-                end
-            end
-            switch Setting.Compare
-                case 'average' 
-                    value = mean(allPerform,1);
-                case 'statistic' 
-                    [~,~,stats] = friedman(allPerform,1,'off');
-                    value = multcompare(stats,'Display','off');
-            end
-        end
-
-        %% design new algorithms based on the current ones
-        [NewOp,NewPara,Aux] = Disturb(obj,Problem,Setting,innerG,Aux)
-
-        %% exactly evaluate algorithms' performance
-        [obj,Solution] = Evaluate(obj,Problem,Data,Setting,indInstance)
-
-        %% approximatly estimate algorithms' performance
-        obj = Estimate(obj,Problem,Setting,indInstance,Surrogate)
-
-        %% select algorithms
-        Algs = Select(obj,Problem,Data,Setting,indInstance)
-
-        function obj = Construct(obj,operator,parameter)
-            obj.operatorPheno  = operator;
-            obj.parameterPheno = parameter;
-        end
-
-        function obj = Construct2(obj,Operators,Paras,Problem,Setting)
-            [Operators,Paras] = obj.Repair(Operators,Paras,Problem,Setting);
-            obj.operator      = Operators;
-            obj.parameter     = Paras;
-            [currOp,currPara] = obj.Decode(Operators,Paras,Problem,Setting);
-            obj.operatorPheno  = currOp;
-            obj.parameterPheno = currPara;
-            obj.performance       = zeros(length(Problem),Setting.AlgRuns);
-            obj.performanceApprox = zeros(length(Problem),Setting.AlgRuns);
-        end
-
-        function value = avePerformAll(obj)
-            value = zeros(length(obj),1);
-            for i = 1:length(obj)
-                value(i) = mean(obj(i).performance,'all');
-            end
-        end
-
-        function value = avePerformApproxAll(obj)
-            value = zeros(length(obj),1);
-            for i = 1:length(obj)
-                value(i) = mean(obj(i).performanceApprox,'all');
-            end
-        end
-
-        function value = avePerformPer(obj,ind)
-            value = zeros(length(obj),1);
-            for i = 1:length(obj)
-                value(i) = mean(obj(i).performance(ind,:));
-            end
-        end
-
-        function value = avePerformApproxPer(obj,ind)
-            value = zeros(length(obj),1);
-            for i = 1:length(obj)
-                value(i) = mean(obj(i).performanceApprox(ind,:));
-            end
-        end
-    end
-
-    methods(Static)        
-        % initialize graph representations of the designed algorithms
-        [Operators,Paras] = Initialize(Problem,Setting,N)
-
-        % repair the designed algorithms to ensure the algorithms' reasonability
-        [Operators,Paras,Change] = Repair(Operators,Paras,Problem,Setting)
-        
-        % decode the designed algorithms from their graph representations
-        [currOp,currPara] = Decode(Operators,Paras,Problem,Setting)
-
-        % encode the action as the graph representation of the algorithm
-        [Operators,Paras] = Encode(Action,Setting,AlgN);
-
-        % search algorithms via heuristics
-        [Algs,AlgTrace] = Search(Problem,Data,Setting,seedTrain,app,bar);
-
-        % learn the algorithm distribution via transformer and reinforcement learning
-        [Algs,AlgTrace] = Learn(Problem,Data,Setting,seedTrain,app,bar);
-    end
-end
\ No newline at end of file
diff --git a/matlab/Utilities/@DESIGN/Decode.m b/matlab/Utilities/@DESIGN/Decode.m
deleted file mode 100644
index b27b89d..0000000
--- a/matlab/Utilities/@DESIGN/Decode.m
+++ /dev/null
@@ -1,84 +0,0 @@
-function [currOp,currPara] = Decode(Operator,Para,Problem,Setting)
-% Decode the designed algorithm from the graph representation.
-% Operator: 1*P, P search pathways
-% Para    : 1*P, P search pathways
-
-%----------------------------Copyright-------------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-AllOp = Setting.AllOp;
-rate      = Setting.IncRate;
-innerGmax = ceil(Setting.Conds/Setting.ProbN);
-
-switch Problem(1).type{1}
-    case 'continuous'
-        indMu = find(contains(AllOp,'search_mu'));
-    case {'discrete','permutation'}
-        indMu = find(contains(AllOp,'search'));
-end
-indCross = find(contains(AllOp,'cross'));
-currOp   = cell(1,Setting.AlgP);
-currPara = cell(1,Setting.AlgP);
-
-for i = 1:Setting.AlgP % for each search pathway
-    currOp{i}.Choose   = AllOp{Operator{i}(1,1)};
-    currOp{i}.Search   = cell(size(Operator{i},1)-1,3); % number of search operators * 3
-    currOp{i}.Update   = AllOp{Operator{i}(end,end)};
-    currOp{i}.Archive  = Setting.Archive;
-
-    currPara{i}.Choose = Para{strcmp(currOp{i}.Choose,AllOp),1};
-    currPara{i}.Search = cell(size(Operator{i},1)-1,2); % number of search operators * 2
-    currPara{i}.Update = Para{strcmp(currOp{i}.Update,AllOp),1};
-    
-    j = 2;
-    while j <= size(Operator{i},1)
-        % set search operators and their parameters
-        thisSearchInd = Operator{i}(j,1);
-        thisSearchIndNext = Operator{i}(j,2);
-        currOp{i}.Search{j-1,1}   = AllOp{thisSearchInd};
-        currPara{i}.Search{j-1,1} = Para{thisSearchInd,1};
-        if ismember(thisSearchInd,indCross) && ismember(thisSearchIndNext,indMu)
-            % put mutation operator to the second column
-            currOp{i}.Search{j-1,2}   = AllOp{thisSearchIndNext}; 
-            currPara{i}.Search{j-1,2} = Para{thisSearchIndNext,1};
-            % set search operators' termination conditions
-            if strcmp(Para{thisSearchInd,2},'GS') || strcmp(Para{thisSearchIndNext,2},'GS')
-                currOp{i}.Search{j-1,3} = [-inf,1]; % global search operator terminates after 1 iteration
-            else
-                currOp{i}.Search{j-1,3} = [rate,innerGmax(thisSearchInd)];
-            end
-            % jump to the row after the next row of the Operator matrix
-            j = j+2;
-        else
-            % set search operators' termination conditions
-            if strcmp(Para{thisSearchInd,2},'GS') 
-                currOp{i}.Search{j-1,3} = [-inf,1]; 
-            else
-                currOp{i}.Search{j-1,3} = [rate,innerGmax(thisSearchInd)];
-            end
-            j = j+1;
-        end
-    end
-
-    % delete empty rows
-    rowDelete = cellfun(@isempty,currOp{i}.Search(:,1));
-    currOp{i}.Search(rowDelete,:) = [];
-    currPara{i}.Search(rowDelete,:) = [];
-end
-end
\ No newline at end of file
diff --git a/matlab/Utilities/@DESIGN/Disturb.m b/matlab/Utilities/@DESIGN/Disturb.m
deleted file mode 100644
index d7fd5e2..0000000
--- a/matlab/Utilities/@DESIGN/Disturb.m
+++ /dev/null
@@ -1,168 +0,0 @@
-function [NewOp,NewPara,Aux]= Disturb(Algs,Setting,innerG,Aux)
-% Design new algorithm(s) based on the current ones.
-
-%----------------------------Copyright-------------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-OpSpace   = Setting.OpSpace;
-ParaSpace = Setting.ParaSpace;
-ParaLocalSpace = Setting.ParaLocalSpace;
-
-% get indices of non-empty parameters
-indNonEmptyPara = find(~cellfun(@isempty,ParaSpace)==1);
-
-NewOp   = cell(Setting.AlgN,Setting.AlgP);
-NewPara = cell(Setting.AlgN,1);
-for i = 1:Setting.AlgN
-    thisOp   = Algs(i).operator;  % 1*P cells
-    thisPara = Algs(i).parameter; % 1*1 cells
-
-    % extract indices of operators and non-empty parameters
-    indSearch = [];
-    for j = 1:Setting.AlgP
-        indSearch = [indSearch;thisOp{j}(2:end,1)];     
-    end    
-    indOp   = [thisOp{1}(1,1);indSearch;thisOp{1}(end,end)]; % indices of operators
-    indPara = indNonEmptyPara(ismember(indNonEmptyPara,indOp)); % indices of parameters
-
-    % determine where to disturb
-    if innerG == 1
-        if Setting.TunePara == false
-            probOp   = 1/(numel(indOp)+numel(indPara)); % probability of disturbing an operator
-            probPara = probOp*numel(indPara); % probability of disturbing all parameters
-            prob     = [repmat(probOp,1,numel(indOp)),probPara];
-            if numel(OpSpace(1,1):OpSpace(1,2)) == 1
-                prob(1) = 0;
-            end
-            if numel(OpSpace(2,1):OpSpace(2,2)) == 1
-                prob(2:numel(indOp)-1) = 0;
-            end
-            if numel(OpSpace(3,1):OpSpace(3,2)) == 1
-                prob(numel(indOp)) = 0;
-            end
-            Aux{i}.seed = randsrc(1,1,[1:numel(indOp)+1;prob./sum(prob)]);
-        else % always disturb parameters
-            Aux{i}.seed = numel(indOp)+1;
-        end
-    end
-
-    % disturb
-    seed = Aux{i}.seed;
-    if seed <= numel(indOp)
-        % disturb an operator
-        if seed == 1
-            % disturb the choose operator
-            indPool = OpSpace(1,1):OpSpace(1,2);
-            indPool(indPool == indOp(seed)) = [];
-            indNew = datasample(indPool,1);
-            for j = 1:Setting.AlgP
-                thisOp{j}(1,1) = indNew;
-            end
-        elseif seed == numel(indOp)
-            % disturb the update operator
-            indPool = OpSpace(3,1):OpSpace(3,2);
-            indPool(indPool == indOp(seed)) = [];
-            indNew = datasample(indPool,1);
-            for j = 1:Setting.AlgP
-                thisOp{j}(end,end) = indNew;
-            end
-        else
-            % disturb the search operator
-            if Setting.AlgP == 1
-                % a single search pathway
-                indPool = OpSpace(2,1):OpSpace(2,2);
-                indPool(indPool==indOp(seed)) = [];
-                indNew  = datasample(indPool,1);
-                if numel(indSearch) == 1 && numel(indSearch) < Setting.AlgQ
-                    samplePool = [indPool,+inf]; % +inf: add an operator after the current one
-                elseif numel(indSearch) > 1 && numel(indSearch) < Setting.AlgQ
-                    samplePool = [indPool,+inf,-inf]; % -inf: delete the current operator
-                elseif numel(indSearch) > 1 && numel(indSearch) == Setting.AlgQ
-                    samplePool = [indPool,-inf];
-                else
-                    samplePool = indPool;
-                end
-                sampleInd = datasample(samplePool,1);
-                if sampleInd == +inf
-                    thisOp{1} = [thisOp{1}(1:seed,:);zeros(1,2);thisOp{1}(seed+1:end,:)];
-                    thisOp{1}(seed+1,:) = [indNew,thisOp{1}(seed,2)];
-                    thisOp{1}(seed,2)   = indNew;
-                elseif sampleInd == -inf
-                    thisOp{1}(seed-1,2) = thisOp{1}(seed,2);
-                    thisOp{1}(seed,:)   = [];
-                else
-                    thisOp{1}(seed,1)   = indNew;
-                    thisOp{1}(seed-1,2) = indNew;
-                end
-            else
-                % multiple search pathways             
-                indPath = [];
-                for j = 1:Setting.AlgP
-                    numSearch = size(thisOp{j},1)-1; % number of search operators in pathway j
-                    indPath = [indPath,repmat(j,1,numSearch)]; % indices of search pahways that search operators belong to
-                end
-                thisPath = indPath(seed-1);
-                indChoose = thisOp{thisPath}(1,1);
-                indUpdate = thisOp{thisPath}(end,end);
-                currQ = randi(Setting.AlgQ); % number of search operators in the (seed-1)-th search pathway
-                thisOp{thisPath} = zeros(currQ+1,2);
-                indSearch = randi([OpSpace(2,1),OpSpace(2,2)]);
-                thisOp{thisPath}(1,:) = [indChoose,indSearch];
-                indSearchStart = indSearch;
-                for k = 2:currQ
-                    indSearchEnd = randi([OpSpace(2,1),OpSpace(2,2)]);
-                    thisOp{thisPath}(k,:) = [indSearchStart,indSearchEnd];
-                    indSearchStart = indSearchEnd;
-                end      
-                thisOp{thisPath}(end,:) = [indSearchStart,indUpdate];
-            end
-        end
-    else
-        % disturb all parameters
-        currPara = [];
-        currParaSpace = [];
-        for j = 1:numel(indPara) % extract parameter values and parameter space
-            for k = 1: numel(thisPara{indPara(j),1}) % for each parameter of operator indPara(j)
-                currPara = [currPara,thisPara{indPara(j),1}(k)];
-            end
-            if strcmp(thisPara{indPara(j),2},'LS')
-                currParaSpace = [currParaSpace;ParaLocalSpace{indPara(j)}];
-            else
-                currParaSpace = [currParaSpace;ParaSpace{indPara(j)}];
-            end
-        end
-        if Setting.AlgN == 1
-            [currPara,Aux{i}] = search_cma(currPara,currParaSpace',Aux{i},'algorithm');
-        else
-            [currPara,~] = search_mu_polynomial(currPara,currParaSpace','algorithm');
-        end
-        % insert new parameters to NewPara
-        for j = 1:numel(indPara)
-            for k = 1:numel(thisPara{indPara(j),1})
-                thisPara{indPara(j),1}(k) = currPara(1);
-                currPara(1) = [];
-            end
-        end
-    end
-
-    for j = 1:Setting.AlgP
-        NewOp{i,j} = thisOp{j};
-    end
-    NewPara{i} = thisPara;
-end
\ No newline at end of file
diff --git a/matlab/Utilities/@DESIGN/Encode.m b/matlab/Utilities/@DESIGN/Encode.m
deleted file mode 100644
index b7e0400..0000000
--- a/matlab/Utilities/@DESIGN/Encode.m
+++ /dev/null
@@ -1,97 +0,0 @@
-function [Operators,Paras] = Encode(Action,Setting,AlgN)
-% Encode the action to the graph representation of the algorithm.
-%----------------------------Copyright-------------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the
-% Free Software Foundation, either version 3 of the License, or any later
-% version.
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-OpSpace   = Setting.OpSpace;
-ParaSpace = Setting.ParaSpace;
-Operators = cell(AlgN,Setting.AlgP); 
-Paras     = cell(AlgN,1);
-Action    = reshape(Action,3,numel(Action)/3)'; % one row for one operator
-
-numChoose = OpSpace(1,2)-OpSpace(1,1)+1;
-numSearch = OpSpace(2,2)-OpSpace(2,1)+1;
-numUpdate = OpSpace(3,2)-OpSpace(3,1)+1;
-
-for i = 1:AlgN
-    %% encode operators to graph edges
-    for j = 1:Setting.AlgP
-        Operators{i,j} = zeros(Setting.AlgQ+1,2);
-        
-        % get operators' indexes
-        indChoose = ceil(Action(1,1)*numChoose);       
-        indSearch = zeros(size(Action,1)-2,1);
-        for k = 1:length(indSearch)
-            indSearch(k) = ceil(Action(k+2,1)*numSearch)+numChoose;
-        end
-        indUpdate = ceil(Action(2,1)*numUpdate)+numChoose+numSearch;
-
-        % encode indexes to graph edges
-        Operators{i,j}(1,:) = [indChoose,indSearch(1)];
-        indSearchStart = indSearch(1);
-        for k = 2:numel(indSearch)
-            indSearchEnd = indSearch(k);
-            Operators{i,j}(k,:) = [indSearchStart,indSearchEnd];
-            indSearchStart = indSearchEnd;
-        end
-        Operators{i,j}(end,:) = [indSearch(end),indUpdate];
-    end
-
-    %% encode parameters
-    % initialize all operators' parameters
-    tempPara = cell(length(ParaSpace),2);
-    indNonEmptyPara = find(~cellfun(@isempty,ParaSpace)==1);
-    for j = 1:numel(indNonEmptyPara)
-        k = indNonEmptyPara(j);
-        tempPara{k,1} = ParaSpace{k}(:,1)+(ParaSpace{k}(:,2)-ParaSpace{k}(:,1)).*rand(size(ParaSpace{k},1),1); 
-    end
-    
-    % replease the involved operators' parameters with ones given by the action
-    j = 1; choosePara = [];
-    while j <= size(ParaSpace{indChoose},1) % for each parameter
-        choosePara = [choosePara;Action(1,1+j)];
-        j = j+1;
-    end
-    if ~isempty(choosePara)
-        tempPara{indChoose,1} = ParaSpace{indChoose}(:,1)+choosePara.*(ParaSpace{indChoose}(:,2)-ParaSpace{indChoose}(:,1));
-    end
-
-    for j = 1:numel(indSearch)
-        k = 1; searchPara = [];
-        while k <= size(ParaSpace{indSearch(j)},1)
-            searchPara = [searchPara;Action(2+j,1+k)];
-            k = k+1;
-        end
-        if ~isempty(searchPara)
-            tempPara{indSearch(j),1} = ParaSpace{indSearch(j)}(:,1)+searchPara.*(ParaSpace{indSearch(j)}(:,2)-ParaSpace{indSearch(j)}(:,1));
-        end
-    end
-
-    j = 1; updatePara = [];
-    while j <= size(ParaSpace{indUpdate},1) 
-        updatePara = [updatePara;Action(2,1+j)];
-        j = j+1;
-    end
-    if ~isempty(updatePara)
-        tempPara{indUpdate,1} = ParaSpace{indUpdate}(:,1)+updatePara.*(ParaSpace{indUpdate}(:,2)-ParaSpace{indUpdate}(:,1));
-    end
-
-    Paras{i} = tempPara;
-end
-end
\ No newline at end of file
diff --git a/matlab/Utilities/@DESIGN/Estimate.m b/matlab/Utilities/@DESIGN/Estimate.m
deleted file mode 100644
index d469e7f..0000000
--- a/matlab/Utilities/@DESIGN/Estimate.m
+++ /dev/null
@@ -1,30 +0,0 @@
-function NewAlgs = Estimate(NewAlgs,Problem,Setting,indInstance,Surrogate)
-% Estimate the designed algorithm's performance by a surrogate model.
-
-%----------------------------Copyright-------------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-EmbedAlgs = Surrogate.UseEmbed(NewAlgs,Setting); 
-
-for i = 1:length(NewAlgs)
-    for j = 1:length(Problem(indInstance))
-        NewAlgs(i).performanceApprox(indInstance(j),:) = predict(Surrogate.model,EmbedAlgs(i,:));
-    end
-end
-end
\ No newline at end of file
diff --git a/matlab/Utilities/@DESIGN/Evaluate.m b/matlab/Utilities/@DESIGN/Evaluate.m
deleted file mode 100644
index 50f711c..0000000
--- a/matlab/Utilities/@DESIGN/Evaluate.m
+++ /dev/null
@@ -1,219 +0,0 @@
-function [NewAlgs,Solution] = Evaluate(NewAlgs,Problem,Data,Setting,seedInstance)
-% Evaluate the designed algorithm's performance. Save all solutions
-% generated during algorithm execution, for calculating landscape features.
-
-%----------------------------Copyright-------------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-%--------------------------------------------------------------------------
-
-Solution = cell(numel(seedInstance),1);
-for i = 1:length(NewAlgs)
-    Operator    = NewAlgs(i).operatorPheno;
-    Parameter   = NewAlgs(i).parameterPheno;
-    Performance = NewAlgs(i).performance;
-    for j = 1:length(Problem(seedInstance))
-        this_ins_Solution = SOLVE; % for saving all solutions that the algorithm generated in the instance
-        switch Problem(seedInstance(j)).type{2}
-            case 'static'
-                for k = 1:Setting.AlgRuns
-                    [ArchSolution,AllSolution,t] = RunDesign(Operator,Parameter,Problem(seedInstance(j)),Data(seedInstance(j)),Setting);
-                    % save result for draw convergence curve in python
-                    if Setting.AlgRuns == 1
-                        %save(['ArchSolution',int2str(Setting.Problem_id)],'ArchSolution');
-                    end
-                    switch Setting.Metric
-                        case 'quality' % solution quality as performance metric
-                            Performance(seedInstance(j),k) = min(ArchSolution.fits); % best performance 
-                        case {'runtimeFE','runtimeSec'} % running time as performance metric
-                            Performance(seedInstance(j),k) = t;
-                        case 'auc' % AUC as performance metric
-                            TimePoints = ceil(Setting.Tmax./Setting.ProbN); % time (Tmax) should be function evaluations when using auc
-                            Performance(seedInstance(j),k) = 1/(sum(ArchSolution(TimePoints).fits <= Setting.Thres')/numel(TimePoints)+eps); % 1/AUC, the smaller the better performance
-                    end
-                    this_ins_Solution = [this_ins_Solution,AllSolution];
-                end
-
-            case 'sequential'
-                for k = 1:Setting.AlgRuns
-                    while Data(seedInstance(j)).continue == true
-                        [ArchSolution,AllSolution,t] = RunDesign(Operator,Parameter,Problem(seedInstance(j)),Data(seedInstance(j)),Setting);
-                        switch Setting.Metric
-                            case 'quality'
-                                Performance(seedInstance(j),k) = Performance(seedInstance(j),k) + min(ArchSolution.fits);
-                            case {'runtimeFE','runtimeSec'}
-                                Performance(seedInstance(j),k) = Performance(seedInstance(j),k) + t;
-                            case 'auc'
-                                TimePoints = ceil(Setting.Tmax./Setting.ProbN);
-                                Performance(seedInstance(j),k) = Performance(seedInstance(j),k) + 1/sum(ArchSolution(TimePoints).fits <= Setting.QuaThres)/numel(TimePoints);
-                        end
-                        % update the current instance of the problem sequence
-                        [~,best] = min(ArchSolution.fits);
-                        [thisProblem,thisData,~] = feval(str2func(Problem(seedInstance(j)).name),Problem(seedInstance(j)),Data(seedInstance(j)),ArchSolution(best),'sequence');
-                        Problem(seedInstance(j)) = thisProblem;
-                        Data(seedInstance(j)) = thisData;
-                        this_ins_Solution = [this_ins_Solution,AllSolution];
-                    end
-                end
-        end
-        this_ins_Solution = this_ins_Solution(2:end); % delete the first empty solution
-        Solution{j} = [Solution{j},this_ins_Solution];
-    end
-    NewAlgs(i).performance = Performance;
-end
-end
-
-function [ArchSolution,AllSolution,t] = RunDesign(Operator,Para,Problem,Data,Setting)
-% Problem.bound: 2*D for continuous and discrete problems, 2*1 for permutation problem
-% Problm.N     : number of solutions to the targeted problem instance
-% Operator     : 1*P, P search pathways
-% Para         : 1*P, P search pathways
-
-% initialize solution(s)
-switch Problem.type{1}
-    case 'continuous'
-        Lower = Problem.bound(1,:);
-        Upper = Problem.bound(2,:);
-        dec   = unifrnd(repmat(Lower,Problem.N,1),repmat(Upper,Problem.N,1));
-    case 'discrete'
-        D     = size(Problem.bound,2); % size of the solution space
-        dec   = zeros(Problem.N,D);
-        for j = 1:D
-            dec(:,j) = randi([Problem.bound(1,j),Problem.bound(2,j)],Problem.N,1);
-        end
-    case 'permutation'
-        [~,dec] = sort(rand(Problem.N,Problem.bound(end)),2);
-end
-Solution = SOLVE(dec,Problem,Data);
-
-% initialize archive(s)
-Archive = cell(length(Operator{1}.Archive),1);
-for i = 1:length(Operator{1}.Archive)
-    [Archive{i},~] = feval(str2func(Operator{1}.Archive{i}),Solution,Problem,'execute');
-end
-ArchSolution = Solution(randi(Problem.N)); % the best solution found at each iteration, Gmax*1
-AllSolution  = Solution; % all solutions found during algorithm execution
-Aux = cell(1,Setting.AlgP); 
-for i = 1:Setting.AlgP
-    Aux{i} = struct; % auxiliary structure array
-end
-
-% iterate
-G = 1;
-t = 0;
-switch Setting.Metric
-    case {'quality','auc'}
-        Tmax  = +inf;
-        Thres = -inf;
-    case 'runtimeFE'
-        Tmax  = ceil(Setting.Tmax./Setting.ProbN); % time changes to interation G
-        Thres = Setting.Thres;
-    case 'runtimeSec'
-        Tmax  = Setting.Tmax;
-        Thres = Setting.Thres;
-end
-if Setting.AlgP == 1 % if the designed algorithm has a single search pathway
-    while G <= Problem.Gmax && t < Tmax && min(ArchSolution.fits) > Thres
-        tic;
-        for i = 1:size(Operator{1}.Search,1) % for each search operation
-            improve = 1;
-            innerG  = 1;
-            while improve(1) >= Operator{1}.Search{i,end}(1) && innerG <= Operator{1}.Search{i,end}(2) % termination condition of search operator i
-                % choose where to search from
-                [ind,~] = feval(str2func(Operator{1}.Choose),Solution,Problem,Para{1}.Choose,Aux{1},G,innerG,Data,'execute');
-
-                % search from the chosen solution(s)
-                [New,Aux{1}] = feval(str2func(Operator{1}.Search{i,1}),Solution(ind),Problem,Para{1}.Search{i,1},Aux{1},G,innerG,Data,'execute');
-                if ~isempty(Operator{1}.Search{i,2}) % if designed a sexual evolutonary algorithm with crossover and mutation
-                    New = SOLVE.RepairSol(New,Problem);
-                    [New,Aux{1}] = feval(str2func(Operator{1}.Search{i,2}),New,Problem,Para{1}.Search{i,2},Aux{1},G,innerG,Data,'execute');
-                end
-                New = SOLVE(New,Problem,Data);
-                AllSolution = [AllSolution,New];
-
-                if strcmp(Operator{1}.Search{i,1},'search_pso') % update pbest and gbest for PSO' particle fly operator
-                    Aux{1} = para_pso(New,Problem,Aux{1});
-                elseif strcmp(Operator{1}.Search{i,1},'search_cma') % update parameters for CMA-ES
-                    Aux{1} = para_cma(New,Problem,Aux{1},'solution');
-                end
-                
-                % update solution(s)
-                [Solution,~] = feval(str2func(Operator{1}.Update),[Solution,New],Problem,Para{1}.Update,Aux{1},G,innerG,Data,'execute');
-
-                % update archive(s)
-                for j = 1:length(Operator{1}.Archive)
-                    [Archive{i},~] = feval(str2func(Operator{1}.Archive{j}),Solution,Archive{i},Problem,'execute');
-                end
-                [ArchSolution,~] = archive_best(Solution,ArchSolution,'execute');
-                
-                improve = ImproveRate(Solution,improve,innerG,'solution');
-                innerG  = innerG+1;
-                G       = G+1;
-                if strcmp(Setting.Metric,'runtimeFE')
-                    t = G;
-                elseif strcmp(Setting.Metric,'runtimeSec')
-                    t = t +toc;
-                end
-                if G > Problem.Gmax || t >= Tmax || min(ArchSolution.fits) <= Thres
-                    break
-                end
-            end
-            if G > Problem.Gmax || t >= Tmax || min(ArchSolution.fits) <= Thres
-                break
-            end
-        end
-    end
-
-elseif Setting.AlgP > 1 % if the designed algorithm has multiple search pathways
-    eachN  = round(Setting.ProbN*(1/Setting.AlgP));
-    innerG = 1;
-    while G <= Problem.Gmax && t < Tmax && min(ArchSolution.fits) > Thres
-        tic;
-        % choose where to search from
-        [ind,~] = feval(str2func(Operator{1}.Choose),Solution,Problem,Para{1}.Choose,Aux{i},G,innerG,Data,'execute');
-
-        % search from the chosen solution(s)
-        allNew = [];
-        for i = 1:Setting.AlgP
-            % determine indices of solutions to be searched from
-            if i == Setting.AlgP
-                currInd = ind;
-            else
-                currInd = ind(1:eachN);
-                ind(1:eachN) = []; % delete used indices
-            end
-            % search
-            [New,Aux{i}] = feval(str2func(Operator{i}.Search{1}),Solution(currInd),Problem,Para{i}.Search{1},Aux{i},G,innerG,Data,'execute');
-            New = SOLVE(New,Problem,Data);
-            if strcmp(Operator{i}.Search{1},'search_pso') % update pbest and gbest for PSO' particle fly operator
-                Aux{i} = para_pso(New,Problem,Aux{i});
-            elseif strcmp(Operator{i}.Search{1},'search_cma') % update parameters for CMA-ES
-                Aux{i} = para_cma(New,Problem,Aux{i},'solution');
-            end
-            allNew = [allNew,New];
-            AllSolution = [AllSolution,allNew];
-        end
-
-        % update solution(s)
-        [Solution,~] = feval(str2func(Operator{1}.Update),[Solution,allNew],Problem,Para{1}.Update,Aux{i},G,innerG,Data,'execute');
-
-        % update archive(s)
-        for i = 1:length(Operator{1}.Archive)
-            [Archive{i},~] = feval(str2func(Operator{1}.Archive{i}),Solution,Archive{i},Problem,'execute');
-        end
-        [ArchSolution,~] = archive_best(Solution,ArchSolution,'execute');
-
-        innerG = innerG+1;
-        G      = G+1;
-        if strcmp(Setting.Metric,'runtimeFE')
-            t = G;
-        elseif strcmp(Setting.Metric,'runtimeSec')
-            t = t + toc;
-        end
-    end
-end
-end
diff --git a/matlab/Utilities/@DESIGN/Initialize.m b/matlab/Utilities/@DESIGN/Initialize.m
deleted file mode 100644
index d570deb..0000000
--- a/matlab/Utilities/@DESIGN/Initialize.m
+++ /dev/null
@@ -1,62 +0,0 @@
-function [Operators,Paras] = Initialize(Setting,N)
-% Initialize the designed algorithm(s).
-
-%----------------------------Copyright-------------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-OpSpace   = Setting.OpSpace;
-ParaSpace = Setting.ParaSpace;
-Operators = cell(N,Setting.AlgP); % N algorithms, each with AlgP search 
-% pathways. Each cell contains a number of AlgP matrix within edges of the graph 
-% representation of a designed algorithm
-Paras     = cell(N,1); % parameters of N algorithms
-for i = 1:N
-    % initialize operators
-    indChoose = randi([OpSpace(1,1),OpSpace(1,2)]); % different search pathways use the same choose operator
-    indUpdate = randi([OpSpace(3,1),OpSpace(3,2)]); % different search pathways use the same update operator
-    for j = 1:Setting.AlgP
-%         currQ = Setting.AlgQ;
-        currQ = randi(Setting.AlgQ); % number of search operators in the current search pathway
-        Operators{i,j} = zeros(currQ+1,2);
-
-        indSearch = randi([OpSpace(2,1),OpSpace(2,2)]);
-        Operators{i,j}(1,:) = [indChoose,indSearch];
-
-        indSearchStart = indSearch;
-        for k = 2:currQ
-            indSearchEnd = randi([OpSpace(2,1),OpSpace(2,2)]);
-            Operators{i,j}(k,:) = [indSearchStart,indSearchEnd];
-            indSearchStart = indSearchEnd;
-        end
-
-        Operators{i,j}(end,:) = [indSearchStart,indUpdate];
-    end
-    % initialize parameters
-    tempPara = cell(length(ParaSpace),2); % first column of each row
-    % contains a cloumn vector of an operator's parameter values,
-    % second column will contain a string of whether the operator
-    % performs local or global search behavior.
-    indNonEmptyPara = find(~cellfun(@isempty,ParaSpace)==1);
-    for j = 1:numel(indNonEmptyPara)
-        k = indNonEmptyPara(j);
-        tempPara{k,1} = ParaSpace{k}(:,1)+(ParaSpace{k}(:,2)-ParaSpace{k}(:,1)).*rand(size(ParaSpace{k},1),1);
-    end
-    Paras{i} = tempPara;
-end
-end
\ No newline at end of file
diff --git a/matlab/Utilities/@DESIGN/Learn.m b/matlab/Utilities/@DESIGN/Learn.m
deleted file mode 100644
index 2d2e671..0000000
--- a/matlab/Utilities/@DESIGN/Learn.m
+++ /dev/null
@@ -1,68 +0,0 @@
-function [Algs,AlgTrace]= Learn(Problem,Data,Setting,seedTrain,app,bar)
-% Learn the algorithm distribution via transformer and reinforcement learning
-%----------------------------Copyright-------------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-str = 'Designing... ';
-if ~isempty(app)
-    app.TextArea.Value = str;
-    drawnow;
-else
-    waitbar(100,bar,str);
-end
-
-% train transformer
-
-
-Algs = getAlg(Action,Problem,Data,Setting,seedTrain); % get the algorithm from the action
-p    = Algs.GetPerformance(Setting,seedTrain); % algorithm's performance
-
-
-% output the final algorithm
-Algs = getAlg(Action,Problem,Data,Setting,seedTrain);
-AlgTrace = Algs;
-end
-
-function Algs = getAlg(Action,Problem,Data,Setting,seedTrain)
-% get the algorithm from the action
-
-AlgN = 1; % number of algorithms to be evaluated
-obj  = DESIGN;
-
-% encode the action as the graph representation of the algorithm
-[Operators,Paras] = obj.Encode(Action,Setting,AlgN);
-
-% repair the algorithm to keep it to be feasible
-[Operators,Paras] = obj.Repair(Operators,Paras,Problem,Setting);
-
-% construct the algorithm struct
-Algs(1,AlgN) = DESIGN;
-for i = 1:AlgN
-    Algs(i).operator  = Operators(i,:);
-    Algs(i).parameter = Paras{i};
-    [currOp,currPara]   = Algs.Decode(Operators(i,:),Paras{i},Problem,Setting);
-    Algs(i).operatorPheno  = currOp;
-    Algs(i).parameterPheno = currPara;
-    Algs(i).performance       = zeros(length(Problem),Setting.AlgRuns);
-    Algs(i).performanceApprox = zeros(length(Problem),Setting.AlgRuns);
-end
-
-% evaluate the algorithm
-Algs = Algs.Evaluate(Problem,Data,Setting,seedTrain);
-end
\ No newline at end of file
diff --git a/matlab/Utilities/@DESIGN/Repair.m b/matlab/Utilities/@DESIGN/Repair.m
deleted file mode 100644
index 920a755..0000000
--- a/matlab/Utilities/@DESIGN/Repair.m
+++ /dev/null
@@ -1,132 +0,0 @@
-function [Operators,Paras,Change] = Repair(Operators,Paras,Problem,Setting)
-% Ensure the designed algorithm(s) to be reasonable.
-
-%----------------------------Copyright-------------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-AllOp = Setting.AllOp;
-ParaLocalSpace = Setting.ParaLocalSpace;
-BehavSpace = Setting.BehavSpace;
-Change = 0;
-Back_operators = Operators;
-
-switch Problem(1).type{1}
-    case 'continuous'
-        indMu = find(contains(AllOp,'search_mu'));
-    case {'discrete','permutation'}
-        indMu = find(contains(AllOp,'search'));
-end
-indCross = find(contains(AllOp,'cross'));
-
-for i = 1:size(Operators,1)
-    for j = 1:Setting.AlgP
-        % crossover should be followed by mutation
-        for k = 2:size(Operators{i,j},1)-1
-            if ismember(Operators{i,j}(k,1),indCross) && ~ismember(Operators{i,j}(k,2),indMu)
-                indThisMu = datasample(indMu,1);
-                Operators{i,j}(k,2) = indThisMu;
-                Operators{i,j}(k+1,1) = indThisMu;
-                %debug = 1
-            end
-        end
-        % crossover should not be the last search operator
-        if ismember(Operators{i,j}(end,1),indCross)
-            thisIndMu = datasample(indMu,1);
-            Operators{i,j}(end,1)   = thisIndMu;
-            Operators{i,j}(end-1,2) = thisIndMu;
-            %debug = 2
-        end
-
-        % delete edges that the starting and end points are the same
-        rowDelete = Operators{i,j}(:,1)==Operators{i,j}(:,2);
-        Operators{i,j}(rowDelete,:) = [];
-
-        % if search_pso is involved, it should be incorporated with choose_traverse and update_always
-        indPSO = find(strcmp(AllOp,'search_pso'));
-        if ismember(indPSO,Operators{i,j})
-            indChoose = find(strcmp(AllOp,'choose_traverse'));
-            indUpdate = find(strcmp(AllOp,'update_always'));
-            % only use the first pathway's choose and update operators in algorithm performance evaluation
-            Operators{i,1}(1,1) = indChoose; 
-            Operators{i,1}(end,end) = indUpdate;
-            Operators{i,j} = [indChoose,indPSO;indPSO,indUpdate];
-            %debug = 3
-        end
-
-        % there should be at most one global search operator in a pathway
-        indSearch = Operators{i,j}(2:end,1); % indices of search operators
-        for k = 1:numel(indSearch)
-            Paras{i}{indSearch(k),2} = 'LS'; % set all search operators' behaviors as local search        
-        end
-        for k = 1:numel(indSearch)
-            if isempty(BehavSpace{indSearch(k)}{2,1})
-                indSearch(k) = 0; 
-            end
-        end
-        indSearch(indSearch==0) = []; % delete operators that only behave local search
-        indGS = []; % indices of global search operators
-        
-        if ~isequal(Back_operators,Operators)
-            Change = 1;
-            return
-        end
-        for k = 1:numel(indSearch)           
-            if isempty(BehavSpace{indSearch(k)}{1,1}) % if operator only behaves global search
-                indGS = [indGS,indSearch(k)];
-            elseif any(Paras{i}{indSearch(k),1} < ParaLocalSpace{indSearch(k)}(:,1)) || any(Paras{i}{indSearch(k),1} > ParaLocalSpace{indSearch(k)}(:,2))
-                % if parameters are smaller than the lower bound or larger
-                % than the upper bound of the parameter space for local
-                % search
-                indGS = [indGS,indSearch(k)];
-            end
-        end
-        if numel(indGS) == 1
-            Paras{i}{indGS,2} = 'GS'; % revise the only global search operator's behavior to global
-        elseif numel(indGS) > 1 % repair the global search operator to behave local search
-            Change = 2;
-            indRetain = indGS(randperm(numel(indGS),1)); % index of the retained global search operator
-            rowRetain = Operators{i,j}(:,1)==indRetain;
-            if ismember(indRetain,indCross) && ismember(Operators{i,j}(rowRetain,2),indMu) && ismember(Operators{i,j}(rowRetain,2),indGS)
-                % if the global search operator is a crossover and the
-                % crossover is followed by a global mutation, then retain 
-                % the mutation.
-                indRetain = [indRetain,Operators{i,j}(rowRetain,2)];
-            end
-            for k = 1:numel(indRetain)
-                Paras{i}{indRetain(k),2} = 'GS'; % change the retained global search operator's behavior to global
-                indGS(indGS==indRetain(k)) = []; % delete the retained global search operator
-            end
-
-            for k = 1:numel(indGS)
-                if ~isempty(ParaLocalSpace{indGS(k)})
-                    % if can perform local search, initialize the parameter
-                    % values from the parameter space for local search
-                    lower = ParaLocalSpace{indGS(k)}(:,1);
-                    upper = ParaLocalSpace{indGS(k)}(:,2);
-                    Paras{i}{indGS(k),1} = lower+(upper-lower).*rand(numel(lower),1);
-                else
-                    % if cannot perform local search, delete the operator
-                    indDel = find(Operators{i,j}(:,2) == indGS(k));
-                    Operators{i,j}(indDel,2) = Operators{i,j}(indDel+1,2);
-                    Operators{i,j}(indDel+1,:) = [];
-                end
-            end
-        end
-    end
-end
\ No newline at end of file
diff --git a/matlab/Utilities/@DESIGN/Search.m b/matlab/Utilities/@DESIGN/Search.m
deleted file mode 100644
index 9b9bf0c..0000000
--- a/matlab/Utilities/@DESIGN/Search.m
+++ /dev/null
@@ -1,139 +0,0 @@
-function [Algs,AlgTrace]= Search(Problem,Data,Setting,seedTrain,app,bar)
-% Search algorithms via heuristics.
-%----------------------------Copyright-------------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-% initialize algorithms and evaluate their performance
-AlgGmax = ceil(Setting.AlgFE/Setting.AlgN);
-switch Setting.Evaluate
-    case {'exact','intensification'}
-        Algs = DESIGN(Problem,Setting);
-        Algs = Algs.Evaluate(Problem,Data,Setting,seedTrain);
-    case 'racing'
-        Algs = DESIGN(Problem,Setting);
-        Algs = Algs.Evaluate(Problem,Data,Setting,seedTrain(1:Setting.RacingK));
-    case 'approximate'
-        Surrogate = Approximate(Problem,Data,Setting,seedTrain); % initialize surrogate model
-        Algs = Surrogate.data(randperm(length(Surrogate.data),Setting.AlgN)); % get initial algorithms
-end
-
-% iterate search
-G = 1;
-AlgTrace = DESIGN; % for save best algorithms found at each iteration
-while G <= AlgGmax
-    str = ['Designing... ',num2str(100*G/AlgGmax),'%'];
-    if ~isempty(app)
-        app.TextArea.Value = str;
-        drawnow;
-    else
-        waitbar(G/AlgGmax,bar,str);
-    end
-    improve = 1;
-    innerG  = 1;
-    Aux     = cell(Setting.AlgN,1); % auxiliary data
-    if Setting.AlgN == 1
-        innerGmax = ceil(Setting.AlgFE/Setting.AlgN/10);
-    else % global search for designing multiple algorithms
-        innerGmax = 1;
-    end
-    while improve(1) >= Setting.IncRate && innerG <= innerGmax
-        % design new algorithms
-        [NewAlgs,Aux] = Algs.GetNew(Problem,Setting,innerG,Aux);
-
-        % performance evaluation and algorithm selection
-        switch Setting.Evaluate
-            case 'exact'
-                NewAlgs = NewAlgs.Evaluate(Problem,Data,Setting,seedTrain);
-                AllAlgs = [Algs,NewAlgs];
-                Algs = AllAlgs.Select(Problem,Data,Setting,seedTrain);
-
-            case 'approximate'
-                % get surrogate
-                NewAlgs = NewAlgs.Estimate(Problem,Setting,seedTrain,Surrogate);
-                AllAlgs = [Algs,NewAlgs];
-                Algs = AllAlgs.Select(Problem,Data,Setting,seedTrain);
-                % update surrogate
-                if ismember(G,Surrogate.exactG)
-                    NewAlgs = NewAlgs.Evaluate(Problem,Data,Setting,seedTrain);
-                    Surrogate = Surrogate.UpdateModel(NewAlgs,Setting);
-                end
-
-            case 'intensification'
-                % screen survivals from new algorithms
-                while ~isempty(NewAlgs) && ~isempty(seedTrain)
-                    NewAlgs = NewAlgs.Evaluate(Problem,Data,Setting,seedTrain(1));
-                    AllAlgs = [Algs,NewAlgs];
-                    NewAlgs = AllAlgs.Select(Problem,Data,Setting,seedTrain(1));
-                    seedTrain(1) = [];
-                end
-                % restore instance indices
-                seedTrain = randperm(numel(instance));
-                % evaluate new incumbents (NewAlgs)' performance on all instances
-                for i = 1:length(NewAlgs)
-                    for j = 1:numel(seedTrain)
-                        if sum(NewAlgs(i).performance(seedTrain(j),:)) == 0 % if haven't evaluated on instance j
-                            NewAlgs(i) = NewAlgs(i).Evaluate(Problem,Data,Setting,seedTrain(j));
-                        end
-                    end
-                end
-                % update incumbent algorithms
-                Algs(randperm(Setting.AlgN,length(NewAlgs))) = NewAlgs;
-
-            case 'racing'
-                % screen survivals from all algorithms (racing)
-                NewAlgs = NewAlgs.Evaluate(Problem,Data,Setting,seedTrain(1:Setting.RacingK));
-                AllAlgs = [Algs,NewAlgs];
-                Algs = AllAlgs.Select(Problem,Data,Setting,seedTrain(1:Setting.RacingK));
-                seedTrain(1:Setting.RacingK) = [];
-                while length(Algs) > Setting.AlgN && ~isempty(seedTrain)
-                    Algs = Algs.Select(Problem,Data,Setting,seedTrain(1));
-                    seedTrain(1) = [];
-                end
-                % restore instance indices
-                seedTrain = randperm(numel(instance));
-                % delete redundant algorithms after racing
-                if length(Algs) > Setting.AlgN
-                    ind = randperm(length(Algs),length(Algs)-Setting.AlgN);
-                    Algs(ind) = [];
-                end
-        end
-
-        % update auxiliary data
-        for i = 1:Setting.AlgN
-            if isfield(Aux{i},'cma_Disturb') % if use CMA-ES
-                Aux{i} = para_cmaes(Algs(i),Problem,Aux{i},'algorithm'); % update CMA-ES's parameters
-            end
-        end
-
-        % record best algorithms at each iteration
-        currCompare = Setting.Compare;
-        Setting.Compare = 'average';
-        currPerform = Algs.GetPerformance(Setting,seedTrain);
-        Setting.Compare = currCompare;
-        [~,best] = min(currPerform);
-        AlgTrace(G) = Algs(best);
-
-        improve = ImproveRate(Algs,improve,innerG,'algorithm');
-        innerG  = innerG+1;
-        G       = G+1;
-        if G > AlgGmax
-            break
-        end
-    end
-end
\ No newline at end of file
diff --git a/matlab/Utilities/@DESIGN/Select.m b/matlab/Utilities/@DESIGN/Select.m
deleted file mode 100644
index e4847b9..0000000
--- a/matlab/Utilities/@DESIGN/Select.m
+++ /dev/null
@@ -1,119 +0,0 @@
-function output = Select(AllAlgs,Problem,Data,Setting,seedInstance)
-% Select promising algorithms.
-
-%----------------------------Copyright-------------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-% get algorithms' performance
-if strcmp(Setting.Evaluate,'racing')
-    for i = 1:length(AllAlgs)
-        for j = 1:numel(seedInstance)
-            if sum(AllAlgs(i).performance(seedInstance(j),:)) == 0 % if haven't evaluate on instance j
-                AllAlgs(i) = AllAlgs(i).Evaluate(Problem,Data,Setting,seedInstance(j));
-            end
-        end
-    end
-end
-c = AllAlgs.GetPerformance(Setting,seedInstance);
-
-% select algorithms
-switch Setting.Evaluate
-    case {'exact','approximate'}
-        if strcmp(Setting.Compare,'average')
-            [~,ind] = sort(c,'ascend'); % small s values refer to better performance
-        else
-            win = zeros(length(AllAlgs),1);
-            for i = 1:length(AllAlgs)
-                ind1 = find(c(:,1)==i); % indices of pairwise comparisions that Alg i stands on the first position
-                ind2 = find(c(:,2)==i);
-                for j = 1:numel(ind1)
-                    if c(ind1(j),4) < 0 && c(ind1(j),6) < 0.05 % Alg i's result is better than the opponent && Alg i and the opponent are different
-                        win(i) = win(i)+1;
-                    end
-                end
-                for j = 1:numel(ind2)
-                    if c(ind2(j),4) > 0 && c(ind2(j),6) < 0.05
-                        win(i) = win(i)+1;
-                    end
-                end
-            end
-            [~,ind] = sort(win,'descend');
-        end
-        AllAlgs = AllAlgs(ind(1:Setting.AlgN));
-        output = AllAlgs;
-
-    case 'intensification'
-        if strcmp(Setting.Compare,'average')
-            [~,ind] = sort(c,'ascend');
-            deleteInd = [];
-            for i = 1:length(ind)
-                if ind(i) > length(OldAlgs) && ~any(ind(i+1:end)<=length(OldAlgs))
-                    deleteInd = [deleteInd,ind(i)];
-                end
-            end 
-            NewAlgs(deleteInd-length(OldAlgs)) = []; % delete new algorithms that are not better than all incumbents
-        else
-            rowInd = find(c(:,1)==length(OldAlgs));
-            rowInd = rowInd(end);
-            c = c(1:rowInd,:); % reserve comparisons between old and new algorithms
-            win = zeros(length(AllAlgs),1);
-            for i = length(OldAlgs)+1:length(AllAlgs) % for each new algorithm
-                ind1 = find(c(:,1)==i); 
-                ind2 = find(c(:,2)==i);
-                for j = 1:numel(ind1)
-                    if c(ind1(j),4) < 0 && c(ind1(j),6) < 0.05 
-                        win(i) = win(i)+1;
-                    end
-                end
-                for j = 1:numel(ind2)
-                    if c(ind2(j),4) > 0 && c(ind2(j),6) < 0.05
-                        win(i) = win(i)+1;
-                    end
-                end
-            end
-            win(1:length(OldAlgs),:) = []; % delete information of old algorithms
-            NewAlgs(win==0) = []; % delete new algorithms that are not better than all incumbents
-        end
-        output = NewAlgs;
-
-    case 'racing'
-        win = zeros(length(AllAlgs),1);
-        for i = 1:length(AllAlgs)
-            ind1 = find(c(:,1)==i);
-            ind2 = find(c(:,2)==i);
-            for j = 1:numel(ind1)
-                if c(ind1(j),4) <= 0 && c(ind1(j),6) < 0.05 % <=: not worse than
-                    win(i) = win(i)+1;
-                end
-            end
-            for j = 1:numel(ind2)
-                if c(ind2(j),4) >= 0 && c(ind2(j),6) < 0.05
-                    win(i) = win(i)+1;
-                end
-            end
-        end
-        notWorseInd = find(win>0);
-        if numel(notWorseInd) < Setting.AlgN
-            worseInd    = find(win==0);
-            notWorseInd = [notWorseInd;datasample(worseInd,Setting.AlgN-numel(notWorseInd),'Replace',false)];
-        end
-        AllAlgs = AllAlgs(notWorseInd);
-        output = AllAlgs;
-end
-end
\ No newline at end of file
diff --git a/matlab/Utilities/@SOLVE/InputAlg.m b/matlab/Utilities/@SOLVE/InputAlg.m
deleted file mode 100644
index 0fc19b7..0000000
--- a/matlab/Utilities/@SOLVE/InputAlg.m
+++ /dev/null
@@ -1,138 +0,0 @@
-function [Alg,Setting] = InputAlg(Setting)
-% Set the algorithm profile for solving the targeted problem.
-
-%----------------------------Copyright-------------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-if ~isempty(Setting.AlgFile) && ~strcmp(Setting.AlgFile,'None')
-    tempAlg = load(Setting.AlgFile);
-    Alg = tempAlg.algs(1);
-    Setting.AlgP = length(Alg.operator);
-else
-    Setting.AlgP = 1;
-    currOp   = cell(1,Setting.AlgP);
-    currPara = cell(1,Setting.AlgP);
-    switch Setting.AlgName        
-        case 'Continuous Genetic Algorithm'
-            currOp{1}.Choose = 'choose_tournament';
-            currOp{1}.Search = {'cross_sim_binary','search_mu_polynomial',[-inf,1]};
-            currOp{1}.Update = 'update_round_robin';
-            currPara{1}.Search = {20,[0.2;20]}; % {crossover distribution;[mutation probability; mutation distribution]}
-        case 'Evolutionary Programming'
-            currOp{1}.Choose = 'choose_tournament';
-            currOp{1}.Search = {'search_mu_gaussian','',[-inf,1]};
-            currOp{1}.Update = 'update_round_robin';
-        case 'Fast Evolutionary Programming'
-            currOp{1}.Choose = 'choose_tournament';
-            currOp{1}.Search = {'search_mu_cauchy','',[-inf,1]};
-            currOp{1}.Update = 'update_round_robin';
-        case 'CMA-ES'
-            currOp{1}.Choose = 'choose_traverse';
-            currOp{1}.Search = {'search_cma','',[-inf,1]};
-            currOp{1}.Update = 'update_greedy';
-        case 'Estimation of Distribution'
-            currOp{1}.Choose = 'choose_traverse';
-            currOp{1}.Search = {'search_eda','',[-inf,1]};
-            currOp{1}.Update = 'update_greedy';
-        case 'Particle Swarm Optimization'
-            currOp{1}.Choose = 'choose_traverse';
-            currOp{1}.Search = {'search_pso','',[-inf,1]};
-            currOp{1}.Update = 'update_pairwise';
-            currPara{1}.Search = {0.9,[]}; % inertia weight 
-        case 'Differential Evolution'
-            currOp{1}.Choose = 'choose_traverse';
-            currOp{1}.Search = {'search_de_current','',[-inf,1]};
-            currOp{1}.Update = 'update_pairwise'; 
-            currPara{1}.Search = {[0.9,0.1],[]}; % {scaling factor, crossover probability}
-        case 'Continuous Random Search'
-            currOp{1}.Choose = 'choose_traverse';
-            currOp{1}.Search = {'reinit_continuous','',[-inf,1]};
-            currOp{1}.Update = 'update_greedy';
-        
-        case 'Discrete Genetic Algorithm'
-            currOp{1}.Choose = 'choose_tournament';
-            currOp{1}.Search = {'cross_point_uniform','search_reset_rand',[-inf,1]};
-            currOp{1}.Update = 'update_round_robin';
-            currPara{1}.Search = {0.2,0.2}; % {crossover probability, reset probability}
-        case 'Discrete Iterative Local Search'
-            currOp{1}.Choose = 'choose_traverse';
-            currOp{1}.Search = {'search_reset_one','',[0.05,10]; % [fitness improve rate,innerGmax]
-                                'reinit_discrete','',[-inf,1]};
-            currOp{1}.Update = 'update_greedy';
-        case 'Discrete Simulated Annealing'
-            currOp{1}.Choose = 'choose_traverse';
-            currOp{1}.Search = {'search_reset_one','',[-inf,1]};
-            currOp{1}.Update = 'update_simulated_annealing'; 
-            currPara{1}.Update = 0.1; % initial temperture 
-        case 'Discrete Random Search'
-            currOp{1}.Choose = 'choose_traverse';
-            currOp{1}.Search = {'reinit_discrete','',[-inf,1]};
-            currOp{1}.Update = 'update_greedy';
-        
-        case 'Permutation Genetic Algorithm'
-            currOp{1}.Choose = 'choose_tournament';
-            currOp{1}.Search = {'cross_order_two','search_swap',[-inf,1]};
-            currOp{1}.Update = 'update_round_robin';
-        case 'Permutation Iterative Local Search'
-            currOp{1}.Choose = 'choose_traverse';
-            currOp{1}.Search = {'search_insert','',[0.05,10];
-                                'reinit_permutation','',[-inf,1]};
-            currOp{1}.Update = 'update_greedy';
-        case 'Permutation Simulated Annealing'
-            currOp{1}.Choose = 'choose_traverse';
-            currOp{1}.Search = {'search_insert','',[-inf,1]};
-            currOp{1}.Update = 'update_simulated_annealing';
-            currPara{1}.Update = 0.1; % initial temperture  
-        case 'Permutation Variable Neighborhood Search'
-            currOp{1}.Choose = 'choose_traverse';
-            currOp{1}.Search = {'search_swap','',[0.05,10];
-                                'search_scramble','',[0.05,10];
-                                'search_insert','',[0.05,10]};
-            currOp{1}.Update = 'update_greedy';
-        case 'Permutation Random Search'
-            currOp{1}.Choose = 'choose_traverse';
-            currOp{1}.Search = {'reinit_permutation','',[-inf,1]};
-            currOp{1}.Update = 'update_greedy';
-        case 'ICA'
-            currOp{1}.Choose = 'choose_ica';
-            currOp{1}.Search = {'search_ica','',[-inf,1]};
-            currOp{1}.Update = 'update_greedy';
-            currPara{1}.Choose = 10; % inertia weight
-            currPara{1}.Search = {[0.6,0.5],[]}; % [p1,alpha]
-    end
-
-    for i = 1:Setting.AlgP
-        if ~isfield(currOp{i},'Archive')
-            currOp{i}.Archive = '';
-        end
-        if ~isfield(currPara{i},'Choose')
-            currPara{i}.Choose = [];
-        end
-        if ~isfield(currPara{i},'Search')
-            currPara{i}.Search = cell(size(currOp{1}.Search,1),2);
-        end
-        if ~isfield(currPara{i},'Update')
-            currPara{i}.Update = [];
-        end
-    end
-
-    Alg = DESIGN;
-    Alg = Alg.Construct(currOp,currPara);
-end
-end
\ No newline at end of file
diff --git a/matlab/Utilities/@SOLVE/RepairSol.m b/matlab/Utilities/@SOLVE/RepairSol.m
deleted file mode 100644
index 6d49328..0000000
--- a/matlab/Utilities/@SOLVE/RepairSol.m
+++ /dev/null
@@ -1,45 +0,0 @@
-function decs = RepairSol(decs,Problem)
-% Repair infeasible solutions.
-
-%----------------------------Copyright-------------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-switch Problem.type{1}
-    case 'continuous'
-        Lower = Problem.bound(1,:);
-        Upper = Problem.bound(2,:);
-        decs  = max(min(decs,Upper),Lower); % limit solutions within decision space
-    case 'discrete'
-        if contains(Problem.setting,'dec_diff') % if elements of a solution should be different with respect to each other
-            [N,D] = size(decs);
-            for i = 1:N
-                [~,ind] = unique(decs(i,:),'stable'); 
-                while numel(ind) < D
-                    DupInd = setdiff(1:D,ind);
-                    for j = 1:numel(DupInd)
-                        decs(i,DupInd(j)) = randperm(Problem.bound(2,DupInd(j)),1);
-                    end
-                    [~,ind] = unique(decs(i,:),'stable'); 
-                end
-            end
-        end
-    case 'permutation'
-        % don't need to do anything       
-end
-end
\ No newline at end of file
diff --git a/matlab/Utilities/@SOLVE/RunAlg.m b/matlab/Utilities/@SOLVE/RunAlg.m
deleted file mode 100644
index 7dcf071..0000000
--- a/matlab/Utilities/@SOLVE/RunAlg.m
+++ /dev/null
@@ -1,236 +0,0 @@
-function [bestSolutions,allSolutions] = RunAlg(Alg,Problem,Data,app,Setting)
-% Solve the targeted problem instances by a designed algorithm.
-
-%----------------------------Copyright-------------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-Operator  = Alg.operatorPheno;
-Parameter = Alg.parameterPheno;
-bestSolutions = SOLVE; % the best solution at the final iteration of each algorithm run
-allSolutions  = SOLVE; % the best solution at each iteration of the best algorithm run (obtains the best solutions among all runs)
-tic;
-str = 'Initializing...';
-if ~isempty(app)
-    app.TextArea.Value = str;
-    drawnow;
-else
-    bar = waitbar(0,str);
-end
-counter = 1;
-for i = 1:length(Problem)
-    currSolutions   = cell(Setting.AlgRuns,1); 
-    fitnessSequence = zeros(Setting.AlgRuns,1);
-    for j = 1:Setting.AlgRuns
-        switch Problem(i).type{2}
-            case 'static'
-                [ArchSolution,t] = RunDesign(Operator,Parameter,Problem(i),Data(i),Setting);
-                currSolutions{j} = ArchSolution(2:end); % best solutions at each iteration of the current algorithm run
-                [~,best] = min(ArchSolution.fits);
-                bestSolutions(i,j) = ArchSolution(best);
-                
-            case 'sequential'
-                currProblem  = Problem(i);
-                currData     = Data(i);
-                currSolution = SOLVE;
-                k = 1;
-                while currData.continue == true
-                    [ArchSolution,t] = RunDesign(Operator,Parameter,currProblem,currData,Setting);
-                    [~,best] = min(ArchSolution.fits);
-                    currSolution(k) = ArchSolution(best);
-                    k = k+1;
-
-                    % update the current instance of the problem sequence
-                    [~,best] = min(ArchSolution.fits);
-                    [currProblem,currData,~] = feval(str2func(Problem(i).name),currProblem,currData,ArchSolution(best),'sequence');
-                end
-                currSolutions{j} = currSolution; % final solutions of all subproblems obtained in the current algorithm run
-                fitnessSequence(j) = sum(currSolution.fits);
-        end        
-        str = ['Solving... ',num2str(100*counter/(length(Problem)*Setting.AlgRuns)),'%'];
-        if ~isempty(app)
-            app.TextArea.Value = str;
-            drawnow;
-        else
-            waitbar(counter/(length(Problem)*Setting.AlgRuns),bar,str);
-        end
-        counter = counter+1;
-    end
-    switch Problem(i).type{2}
-        case 'static'
-            [~,best] = min(bestSolutions(i,:).fits);
-            allSolutions(i,1:length(currSolutions{1})) = currSolutions{best}; % solutions at each iteration of the best algorithm run
-        case 'sequential'
-            [~,best] = min(fitnessSequence);
-            allSolutions(i,1:length(currSolutions{1})) = currSolutions{best}; % solutions at the final iteration of each subproblem of the best algorithm run         
-    end
-end
-str = 'Complete';
-if ~isempty(app)
-    app.TextArea.Value = str;
-    drawnow;
-else
-    waitbar(100,bar,str);
-end
-toc;
-end
-
-function [ArchSolution,t] = RunDesign(Operator,Para,Problem,Data,Setting)
-% initialize solution(s)
-switch Problem.type{1}
-    case 'continuous'
-        Lower = Problem.bound(1,:);
-        Upper = Problem.bound(2,:);
-        dec   = unifrnd(repmat(Lower,Problem.N,1),repmat(Upper,Problem.N,1));
-    case 'discrete'
-        D     = size(Problem.bound,2); % size of the solution space
-        dec   = zeros(Problem.N,D);
-        for j = 1:D
-            dec(:,j) = randi([Problem.bound(1,j),Problem.bound(2,j)],Problem.N,1);
-        end
-    case 'permutation'
-        [~,dec] = sort(rand(Problem.N,Problem.bound(end)),2);
-end
-Solution = SOLVE(dec,Problem,Data);
-
-% initialize archive(s)
-Archive = cell(length(Operator{1}.Archive),1);
-for i = 1:length(Operator{1}.Archive)
-    [Archive{i},~] = feval(str2func(Operator{1}.Archive{i}),Solution,Problem,'execute');
-end
-ArchSolution = Solution(randi(Problem.N)); % the best solution found at each iteration, Gmax*1
-Aux = cell(1,Setting.AlgP);
-for i = 1:Setting.AlgP
-    Aux{i} = struct; % auxiliary structure array
-end
-
-% iterate
-G = 1;
-t = 0;
-switch Setting.Metric
-    case {'quality','auc'}
-        Tmax  = +inf;
-        Thres = -inf;
-    case 'runtimeFE'
-        Tmax  = ceil(Setting.Tmax./Setting.ProbN); % time changes to interation G
-        Thres = Setting.Thres;
-    case 'runtimeSec'
-        Tmax  = Setting.Tmax;
-        Thres = Setting.Thres;
-end
-if Setting.AlgP == 1 % if the designed algorithm has a single search pathway
-    while G <= Problem.Gmax && t < Tmax && min(ArchSolution.fits) > Thres
-        tic;
-        for i = 1:size(Operator{1}.Search,1)
-            improve = 1;
-            innerG  = 1;
-            while improve(1) >= Operator{1}.Search{i,end}(1) && innerG <= Operator{1}.Search{i,end}(2) % termination condition of search operator i
-                % choose where to search from
-                [ind,~] = feval(str2func(Operator{1}.Choose),Solution,Problem,Para{1}.Choose,Aux{1},G,innerG,Data,'execute');
-
-
-                % search from the chosen solution(s)
-                [New,Aux{1}] = feval(str2func(Operator{1}.Search{i,1}),Solution(ind),Problem,Para{1}.Search{i,1},Aux{1},G,innerG,Data,'execute');
-                
-                if ~isempty(Operator{1}.Search{i,2}) % if designed a sexual evolutonary algorithm with crossover and mutation
-                    New = SOLVE.RepairSol(New,Problem);
-                    [New,Aux{1}] = feval(str2func(Operator{1}.Search{i,2}),New,Problem,Para{1}.Search{i,2},Aux{1},G,innerG,Data,'execute');
-                end
-                New = SOLVE(New,Problem,Data);
-
-                if strcmp(Operator{1}.Search{i,1},'search_pso') % update pbest and gbest for PSO' particle fly operator
-                    Aux{1} = para_pso(New,Problem,Aux{1});
-                elseif strcmp(Operator{1}.Search{i,1},'search_cma') % update parameters for CMA-ES
-                    Aux{1} = para_cma(New,Problem,Aux{1},'solution');
-                end
-
-                % update solution(s)
-                [Solution,~] = feval(str2func(Operator{1}.Update),[Solution,New],Problem,Para{1}.Update,Aux{1},G,innerG,Data,'execute');
-
-                % update archive(s)
-                for j = 1:length(Operator{1}.Archive)
-                    [Archive{i},~] = feval(str2func(Operator{1}.Archive{j}),Solution,Archive{i},Problem,'execute');
-                end
-                [ArchSolution,~] = archive_best(Solution,ArchSolution,'execute');
-
-                improve = ImproveRate(Solution,improve,innerG,'solution');
-                innerG  = innerG+1;
-                G       = G+1;
-                if strcmp(Setting.Metric,'runtimeFE')
-                    t = G;
-                elseif strcmp(Setting.Metric,'runtimeSec')
-                    t = t +toc;
-                end
-                if G > Problem.Gmax || t >= Tmax || min(ArchSolution.fits) <= Thres
-                    break
-                end
-            end
-            if G > Problem.Gmax || t >= Tmax || min(ArchSolution.fits) <= Thres
-                break
-            end
-        end
-    end
-
-elseif Setting.AlgP > 1 % if the designed algorithm has multiple search pathways
-    eachN  = round(Setting.ProbN*(1/Setting.AlgP));
-    innerG = 1;
-    while G <= Problem.Gmax && t < Tmax && min(ArchSolution.fits) > Thres
-        tic;
-        % choose where to search from
-        [ind,~] = feval(str2func(Operator{1}.Choose),Solution,Problem,Para{1}.Choose,Aux{i},G,innerG,Data,'execute');
-
-        % search from the chosen solution(s)
-        allNew = [];
-        for i = 1:Setting.AlgP
-            % determine indices of solutions to be searched from
-            if i == Setting.AlgP
-                currInd = ind;
-            else
-                currInd = ind(1:eachN);
-                ind(1:eachN) = []; % delete used indices
-            end
-            % search
-            [New,Aux{i}] = feval(str2func(Operator{i}.Search{1}),Solution(currInd),Problem,Para{i}.Search{1},Aux{i},G,innerG,Data,'execute');
-            New = SOLVE(New,Problem,Data);
-            if strcmp(Operator{i}.Search{1},'search_pso') % update pbest and gbest for PSO' particle fly operator
-                Aux{i} = para_pso(New,Problem,Aux{i});
-            elseif strcmp(Operator{i}.Search{1},'search_cma') % update parameters for CMA-ES
-                Aux{i} = para_cma(New,Problem,Aux{i},'solution');
-            end
-            allNew = [allNew,New];
-        end
-
-        % update solution(s)
-        [Solution,~] = feval(str2func(Operator{1}.Update),[Solution,allNew],Problem,Para{1}.Update,Aux{i},G,innerG,Data,'execute');
-
-        % update archive(s)
-        for i = 1:length(Operator{1}.Archive)
-            [Archive{i},~] = feval(str2func(Operator{1}.Archive{i}),Solution,Archive{i},Problem,'execute');
-        end
-        [ArchSolution,~] = archive_best(Solution,ArchSolution,'execute');
-
-        innerG = innerG+1;
-        G      = G+1;
-        if strcmp(Setting.Metric,'runtimeFE')
-            t = G;
-        elseif strcmp(Setting.Metric,'runtimeSec')
-            t = t +toc;
-        end
-    end
-end
-end
\ No newline at end of file
diff --git a/matlab/Utilities/@SOLVE/SOLVE.m b/matlab/Utilities/@SOLVE/SOLVE.m
deleted file mode 100644
index 1e5971d..0000000
--- a/matlab/Utilities/@SOLVE/SOLVE.m
+++ /dev/null
@@ -1,111 +0,0 @@
-% Class for solving the targeted problem.
-
-%----------------------------Copyright-------------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-classdef SOLVE < handle
-    properties(SetAccess = private)
-        dec; % decision variables
-        obj; % objective values
-        con; % constraint violations
-        fit; % solution's fitness (considering constraint violations)
-        acc; % accessory data 
-    end
-    
-    methods
-        % construct solutions
-        function solution = SOLVE(dec,Problem,Data)
-            if nargin > 0
-                solution(1,size(dec,1)) = SOLVE;
-                dec = solution.RepairSol(dec,Problem);
-                switch Problem.type{3}
-                    case 'certain' 
-                        dec = feval(str2func(Problem.name),Data,dec,'repair'); % repair solutions   
-                        %Problem.problem_id=3;
-                        [obj,con,acc] = feval(str2func(Problem.name),Data,dec,Problem.problem_id,'evaluate'); % evaluate fitness                    
-                    case 'uncertain' % for uncertain problems
-                        obj = zeros(Problem.sampleN,1);
-                        con = zeros(Problem.sampleN,1);
-                        for i = 1:ProblemsampleN
-                            [obj(i),con(i),acc] = feval(str2func(Problem.name),Data,dec,'evaluate');
-                        end
-                        if contains(Problem.setting,'uncertain_average')
-                            obj = mean(obj,1);
-                            con = mean(con,1);
-                        elseif contains(Problem.setting,'uncertain_worst')
-                            obj = max(obj,[],1);
-                            con = max(con,[],1);
-                        end 
-                end
-                for i = 1:length(solution)
-                    solution(i).dec = dec(i,:);
-                    solution(i).obj = obj(i,:);
-                    if ~isempty(con)
-                        solution(i).con = con(i,:);
-                    else
-                        solution(i).con = 0;
-                    end
-                    Con = sum(max(0,solution(i).con));
-                    feasible = Con<= 0;
-                    %solution(i).fit = feasible.*solution(i).obj+~feasible.*(Con+1e8);
-                    solution(i).fit = solution(i).obj+~feasible.*(Con);
-                    if ~isempty(acc)
-                        solution(i).acc = cell(1,length(acc));
-                        for j = 1:length(acc)
-                            if iscell(acc{j})
-                                solution(i).acc{j} = acc{j}{i};
-                            elseif ismatrix(acc{j})
-                                solution(i).acc{j} = acc{j}(i,:);
-                            else
-                                error('Accessory data should be saved in cell or matrix format.');
-                            end
-                        end
-                    end
-                end
-            end     
-        end
-        
-        function value = decs(solution)
-            value = cat(1,solution.dec);
-        end
-        
-        function value = objs(solution)
-            value = cat(1,solution.obj);
-        end
-
-        function value = cons(solution)
-            value = cat(1,solution.con);
-        end
-
-        function value = fits(solution)
-            value = cat(1,solution.fit);
-        end
-    end
-
-    methods(Static)
-        % ensure solutions to be feasible 
-        dec = RepairSol(dec,Problem)
-
-        % input algorithm
-        [Alg,Setting] = InputAlg(Setting)
-
-        % solve the targeted problem instances by the designed algorithm(s).
-        [bestSolutions,allSolutions] = RunAlg(Alg,ProblemSolve,DataSolve,app,Setting)
-    end
-end
\ No newline at end of file
diff --git a/matlab/Utilities/Others/APP/APP.mlapp b/matlab/Utilities/Others/APP/APP.mlapp
deleted file mode 100644
index c3215ee..0000000
Binary files a/matlab/Utilities/Others/APP/APP.mlapp and /dev/null differ
diff --git a/matlab/Utilities/Others/APP/Open.jpeg b/matlab/Utilities/Others/APP/Open.jpeg
deleted file mode 100644
index 41504c1..0000000
Binary files a/matlab/Utilities/Others/APP/Open.jpeg and /dev/null differ
diff --git a/matlab/Utilities/Others/APP/Run.png b/matlab/Utilities/Others/APP/Run.png
deleted file mode 100644
index 4867957..0000000
Binary files a/matlab/Utilities/Others/APP/Run.png and /dev/null differ
diff --git a/matlab/Utilities/Others/Approximate.m b/matlab/Utilities/Others/Approximate.m
deleted file mode 100644
index 26e44ac..0000000
--- a/matlab/Utilities/Others/Approximate.m
+++ /dev/null
@@ -1,97 +0,0 @@
-% Class for estimating the designed algorithm's performance.
-
-%----------------------------Copyright-------------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-classdef Approximate < handle
-    properties
-        data;
-        embedding;
-        model;
-        randSeed;
-        exactG;
-    end
-
-    methods
-        function obj = Approximate(Problem,Data,Setting,indInstance)
-            if nargin > 0
-                obj = Approximate;
-                % seed for disrupting the order of elements in the vector representation of algorithms
-                if Setting.AlgP == 1 % one search pathway
-                    obj.randSeed = randperm((Setting.AlgQ+2)*3);
-                else % multiple search pathways
-                    obj.randSeed = randperm((Setting.AlgP+2)*3);
-                end
-
-                % determine iterations with exact performance estimations
-                ExactGmax  = Setting.Surro/Setting.AlgN;
-                AlgGmax    = ceil(Setting.AlgFE/Setting.AlgN);
-                obj.exactG = 1:AlgGmax/ExactGmax:AlgGmax;
-
-                % get training data of surrogate
-                TrainAlgs1 = DESIGN(Problem,Setting,500);
-                obj.data = TrainAlgs1.Evaluate(Problem,Data,Setting,indInstance); % exactly evaluate performance
-
-                % train embedding
-                TrainAlgs2 = DESIGN(Problem,Setting,1000);
-                obj.embedding = obj.GetEmbed(TrainAlgs2,Setting);
-                
-                % train surrogate
-                obj.model = obj.GetModel(obj.data,Setting);
-            end
-        end
-
-        function EmbedMap = GetEmbed(obj,data,Setting)
-            EmbedMap  = Embedding(data,Setting,obj,'get');
-        end
-
-        function EmbedAlgs = UseEmbed(obj,data,Setting)
-            EmbedAlgs = Embedding(data,Setting,obj,'use');
-        end
-
-        function Model = GetModel(obj,data,Setting)
-            % train a random forest surrogate to estimate algorithms' performance
-            EmbedAlgs = Embedding(data,Setting,obj,'use'); % get the embeded algorithms
-            Labels    = data.avePerformAll; % all algorithms' average performance on all instances
-            Model     = TreeBagger(1000,EmbedAlgs,Labels,'Method','regression','MinLeafSize',5); % train a random forest surrogate
-        end
-
-        function obj = UpdateModel(obj,Algs,Setting)
-            % Update the surrogate model during the design process.
-            N = Setting.AlgN;
-            a = Algs.avePerformAll;
-            b = Algs.avePerformApproxAll;
-            total  = 0;
-            error  = 0;
-            indNew = [];
-            for i = 1:N
-                for j = i+1:N
-                    res = xor(a(i)<a(j),b(i)<b(j)); % res=xor(A,B)=1 if A is different with B; res=0 otherwise
-                    if res == 1
-                        error = error+1;
-                        indNew = [indNew,i,j];
-                    end
-                    total = total+1;
-                end
-            end
-            newData = Algs(unique(indNew));
-            obj.data  = [obj.data,newData];
-            obj.model = obj.GetModel(obj.data,Setting);
-        end
-    end
-end
\ No newline at end of file
diff --git a/matlab/Utilities/Others/Embedding.m b/matlab/Utilities/Others/Embedding.m
deleted file mode 100644
index e339339..0000000
--- a/matlab/Utilities/Others/Embedding.m
+++ /dev/null
@@ -1,154 +0,0 @@
-function output = Embedding(Algs,Setting,Surrogate,mode)
-% Embed the designed algorithm into a compact vector representation by 
-% auto-encoder.
-
-%----------------------------Copyright-------------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-randSeed = Surrogate.randSeed;
-layer    = 2;   % number of auto-encoder layers
-p        = 0.3; % corrupt probability
-lower    = 1;
-upper    = size(Algs(1).parameter,1);
-
-% get vector representations of the designed algorithms
-if Setting.AlgP == 1 % one search pathway
-    for i = 1:length(Algs)
-        operator = zeros(1,Setting.AlgQ+2); % vector representation of operators
-        operator(1) = Algs(i).operator{1}(1,1);
-        operator(2:size(Algs(i).operator{1},1)) = Algs(i).operator{1}(2:end,1);
-        operator(end) = Algs(i).operator{1}(end,2);
-
-        parameter = zeros(2,Setting.AlgQ+2); % vector representation of parameters
-        for j = 1:Setting.AlgQ+2
-            if operator(j) ~= 0
-                for k = 1:numel(Algs(i).parameter{operator(j),1})
-                    parameter(k,j) = Algs(i).parameter{operator(j),1}(k);
-                end
-            end
-        end
-        parameter = reshape(parameter,1,(Setting.AlgQ+2)*2);
-
-        operator = (operator-min(operator))./(max(operator)-min(operator)); % normilize to [0,1]
-        tempAlg  = [operator,parameter];
-        tempAlg  = tempAlg(randSeed); % disrupt the order of elements in the vector representation
-        tempAlg  = reshape(tempAlg,numel(randSeed)/3,3); % for reducting dimensionality of the vector representation
-        VectorAlgs(:,3*i-2:3*i) = tempAlg;
-    end
-else % multiple search pathways
-    for i = 1:length(Algs)
-        operator = zeros(1,Setting.AlgP+2);
-        operator(1) = Algs(i).operator{1}(1,1);
-        for j = 1:Setting.AlgP
-            operator(j+1) = Algs(i).operator{j}(2,1);
-        end
-        operator(end) = Algs(i).operator{1}(end,2);
-        
-        parameter = zeros(2,Setting.AlgP+2); % each operator has at most 2 parameters
-        for j = 1:numel(Algs(i).parameter{operator(1),1}) % choose operator's parameter(s)
-            parameter(j,1) = Algs(i).parameter{operator(1),1}(j);
-        end
-        for j = 1:Setting.AlgP % search operators' parameter(s)
-            for k = 1:numel(Algs(i).parameter{operator(j+1),1})
-                parameter(k,j+1) = Algs(i).parameter{operator(j+1),1}(k);
-            end
-        end
-        for j = 1:numel(Algs(i).parameter{operator(end),1}) % update operator's parameter(s)
-            parameter(j,end) = Algs(i).parameter{operator(end),1}(j);
-        end
-        parameter = reshape(parameter,1,(Setting.AlgP+2)*2);
-
-        operator = (operator-lower)./(upper-lower); % normilize to [0,1]
-        tempAlg  = [operator,parameter];
-        tempAlg  = tempAlg(randSeed); % disrupt the order of elements in the vector representation
-        tempAlg  = reshape(tempAlg,numel(randSeed)/3,3); % for reducting dimensionality of the vector representation
-        VectorAlgs(:,3*i-2:3*i) = tempAlg;
-    end
-end
-
-switch mode
-    case 'get' % get the embedding mapping by mSDA
-        [EmbedMap,~] = mSDA(VectorAlgs,p,layer);
-        output = EmbedMap; 
-    case 'use' % use the mapping to embed algorithms
-        EmbedMap = Surrogate.embedding;
-        temp     = [VectorAlgs;ones(1,length(Algs)*3)];
-        wx       = EmbedMap(:,:,1)*temp;
-        argslist = zeros(numel(randSeed)/3,length(Algs));
-        for i = 1:length(Algs)
-            argslist(:,i) = wx(:,3*i-2)+wx(:,3*i-1)+wx(:,3*i);
-        end
-        wx = argslist/3;
-        wx = tanh(wx);
-        layer = size(EmbedMap,3);
-        for i = 2:layer
-            wx = [wx;ones(1,length(Algs))];
-            wx = EmbedMap(:,:,i)*wx;
-            wx = tanh(wx);
-        end
-        EmbedAlgs = wx';
-        output = EmbedAlgs;
-end
-end
-
-function [Ws,hs] = mSDA(X,p,l)
-[d,n] = size(X);
-n  = n/3;
-Ws = zeros(d,d+1,l);
-hs = zeros(d,n,l+1);
-%hs(:,:,1) = X;
-[Ws(:,:,1),hs(:,:,1+1)] = mDA_de(X,p);
-for t = 2:l
-    [Ws(:,:,t),hs(:,:,t+1)] = mDA(hs(:,:,t),p);
-end
-end
-
-function [W,h] = mDA_de(X,p)
-X = [X;ones(1,size(X,2))];
-d = size(X,1);
-q = [ones(d-1,1).*(1-p); 1];
-S = X*X';
-Q = S.*(q*q');
-Q(1:d+1:end) = q.*diag(S);
-P = S.*repmat(q',d,1);
-W = P(1:end-1,:)/(Q+1e-5*eye(d));
-
-length = size(X,2)/3;
-h = zeros(d-1,length);
-wx = W*X;
-for i = 1:length
-    temp = wx(:,3*i-2:3*i);
-    temp = temp(:,1)+temp(:,2)+temp(:,3);
-    h(:,i) = temp/3;
-end
-h = tanh(h);
-%h = tanh(W*X);
-end
-
-function [W,h] = mDA(X,p)
-X = [X;ones(1,size(X,2))];
-d = size(X,1);
-q = [ones(d-1,1).*(1-p); 1];
-S = X*X';
-Q = S.*(q*q');
-Q(1:d+1:end) = q.*diag(S);
-P = S.*repmat(q',d,1);
-W = P(1:end-1,:)/(Q+1e-5*eye(d));
-h = tanh(W*X);
-end
diff --git a/matlab/Utilities/Others/ImproveRate.m b/matlab/Utilities/Others/ImproveRate.m
deleted file mode 100644
index 5c6851c..0000000
--- a/matlab/Utilities/Others/ImproveRate.m
+++ /dev/null
@@ -1,51 +0,0 @@
-function improve = ImproveRate(Solution,improve,innerG,type)
-% Calculate the designed algorithm's performance improvement rate during k
-% consecutive iterations.
-
-%----------------------------Copyright-------------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-k = 3;
-
-% initialize
-if innerG == 1
-    improve = [1,zeros(1,k)]; % [fitness improve rate, fitness 1, 2, ..., k]
-end
-
-% keep the best fitness found at the latest k iteration in the improve vector
-switch type
-    case 'solution'
-        Con      = sum(max(0,Solution.cons),2);
-        Feasible = Con <= 0;
-        Fitness  = Feasible.*Solution.objs + ~Feasible.*(Con+1e8);
-    case 'algorithm'
-        Fitness    = Solution.avePerformAll; 
-end
-improve    = [improve,min(Fitness)];
-improve(2) = [];
-
-% calculate the fitness improve rate
-if innerG >= k
-    tempRate = zeros(1,k-1);
-    for i = 2:k
-        tempRate(i-1) = (improve(i)-improve(i+1))./improve(i);
-    end
-    improve(1) = max(tempRate);
-end
-end
\ No newline at end of file
diff --git a/matlab/Utilities/Others/Input.m b/matlab/Utilities/Others/Input.m
deleted file mode 100644
index bd825da..0000000
--- a/matlab/Utilities/Others/Input.m
+++ /dev/null
@@ -1,270 +0,0 @@
-function varargout = Input(varargin)
-% Process the input of algorithm design.
-
-%----------------------------Copyright-------------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-switch varargin{end}
-    case 'data'
-        value   = varargin{1};
-        Setting = varargin{2};
-        % input problem
-        if any(strcmp(value,'Problem'))
-            ind = find(strcmp(value,'Problem'));
-            prob  = value{ind+1};
-            varargout{1} = prob;
-        else
-            error('Please set the targeted problem.');
-        end
-        if strcmp(Setting.Mode,'design') && any(strcmp(value,'InstanceTrain')) && any(strcmp(value,'InstanceTest'))
-            ind = find(strcmp(value,'InstanceTrain'));
-            instanceTrain = value{ind+1}; % indexes of problem instances for designing algorithm
-            ind = find(strcmp(value,'InstanceTest'));
-            instanceTest  = value{ind+1}; % indexes of problem instances for testing the designed algorithm
-            varargout{2} = instanceTrain;
-            varargout{3} = instanceTest;
-        elseif strcmp(Setting.Mode,'solve') && any(strcmp(value,'InstanceSolve'))
-            ind = find(strcmp(value,'InstanceSolve'));
-            instanceSolve = value{ind+1}; % indexes of problem instances to be solved
-            varargout{2} = instanceSolve;
-        else
-            error('Please set the targeted problem instance indexes.');
-        end
-
-    case 'parameter'
-        value   = varargin{1};
-        Setting = varargin{2};
-
-        if any(strcmp(value,'AlgP'))
-            ind = find(strcmp(value,'AlgP'));
-            Setting.AlgP = value{ind+1};
-        end
-        if any(strcmp(value,'AlgQ'))
-            ind = find(strcmp(value,'AlgQ'));
-            Setting.AlgQ = value{ind+1};
-        end
-        if any(strcmp(value,'Archive'))
-            ind = find(strcmp(value,'Archive'));
-            Setting.Archive = value{ind+1};
-        end
-        if any(strcmp(value,'LSRange'))
-            ind = find(strcmp(value,'LSRange'));
-            Setting.LSRange = value{ind+1};
-        end
-        if any(strcmp(value,'IncRate'))
-            ind = find(strcmp(value,'IncRate'));
-            Setting.IncRate = value{ind+1};
-        end
-        if any(strcmp(value,'ProbN'))
-            ind = find(strcmp(value,'ProbN'));
-            Setting.ProbN = value{ind+1};
-        end
-        if any(strcmp(value,'ProbFE'))
-            ind = find(strcmp(value,'ProbFE'));
-            Setting.ProbFE = value{ind+1};
-        end
-        if any(strcmp(value,'InnerFE'))
-            ind = find(strcmp(value,'InnerFE'));
-            Setting.InnerFE = value{ind+1};
-        end
-        if any(strcmp(value,'AlgN'))
-            ind = find(strcmp(value,'AlgN'));
-            Setting.AlgN = value{ind+1};
-        end
-        if any(strcmp(value,'AlgFE'))
-            ind = find(strcmp(value,'AlgFE'));
-            Setting.AlgFE = value{ind+1};
-        end
-        if any(strcmp(value,'AlgRuns'))
-            ind = find(strcmp(value,'AlgRuns'));
-            Setting.AlgRuns = value{ind+1};
-        end
-        if any(strcmp(value,'Metric'))
-            ind = find(strcmp(value,'Metric'));
-            Setting.Metric = value{ind+1};
-        end
-        if any(strcmp(value,'Compare'))
-            ind = find(strcmp(value,'Compare'));
-            Setting.Compare = value{ind+1};
-        end
-        if any(strcmp(value,'Evaluate'))
-            ind = find(strcmp(value,'Evaluate'));
-            Setting.Evaluate = value{ind+1};
-        end
-        if any(strcmp(value,'Tmax'))
-            ind = find(strcmp(value,'Tmax'));
-            Setting.Tmax = value{ind+1};
-        end
-        if any(strcmp(value,'Thres'))
-            ind = find(strcmp(value,'Thres'));
-            Setting.Thres = value{ind+1};
-        end
-        if any(strcmp(value,'RacingK'))
-            ind = find(strcmp(value,'RacingK'));
-            Setting.RacingK = value{ind+1};
-        end
-        if any(strcmp(value,'Surro'))
-            ind = find(strcmp(value,'Surro'));
-            Setting.Surro = value{ind+1};
-        end
-        if any(strcmp(value,'AlgFile'))
-            ind = find(strcmp(value,'AlgFile'));
-            Setting.AlgFile = value{ind+1};
-        end
-        if any(strcmp(value,'AlgName'))
-            ind = find(strcmp(value,'AlgName'));
-            Setting.AlgName = value{ind+1};
-        end
-        varargout{1} = Setting;
-
-    case 'check'
-        Setting = varargin{1};
-        switch Setting.Mode
-            case 'design'
-                % AlgP or AlgQ should be equal to 1
-                if Setting.AlgP > 1 && Setting.AlgQ > 1
-                    error(['Setting.AlgP or Setting.AlgQ should be equal to 1 - ' ...
-                        'For algorithms with multiple search pathways (AlgP>1), ' ...
-                        'each search pathway should have only one search operator (AlgQ=1). ' ...
-                        'For algorithms with a single search pathway (AlgP=1), ' ...
-                        'the search pathway can have multiple search operators (AlgQ>=1).'])
-                end
-
-                % set Tmax and Thres when using runtimeFE or runtimeSec as the performance metric
-                if strcmp(Setting.Metric,'runtimeFE') && isempty(Setting.Tmax)
-                    Setting.Tmax = Setting.ProbFE;
-                end
-                if strcmp(Setting.Metric,'runtimeFE') && isempty(Setting.Thres)
-                    error(['Please set "Setting.Thres" as the lowest acceptable performance ' ...
-                        'of the design algorithms, the performance can be the solution quality.'])
-                end
-                if strcmp(Setting.Metric,'runtimeSec') && isempty(Setting.Tmax)
-                    error('Please set "Setting.Tmax" as the maximum runtime (seconds).')
-                end
-                if strcmp(Setting.Metric,'runtimeSec') && isempty(Setting.Thres)
-                    error(['Please set "Setting.Thres" as the lowest acceptable performance ' ...
-                        'of the design algorithms, the performance can be the solution quality.'])
-                end
-
-                %  time (Tmax) should be function evaluations when using auc
-                if strcmp(Setting.Metric,'auc') && numel(Setting.Tmax) <= 1
-                    error(['"Setting.Tmax" should contain multiple time points. The time ' ...
-                        'points should the numbers of function evaluations spent during ' ...
-                        'the alorithm execution.'])
-                end
-
-                % thresholds should be corresponding to time points when using auc
-                if strcmp(Setting.Metric,'auc') && numel(Setting.Thres) ~= numel(Setting.Tmax)
-                    error(['The number of thresholds in "Setting.Thres" should be equal to ' ...
-                        'the number of time points in "Setting.Tmax". "Setting.Thres" ' ...
-                        'refers to the lowest acceptable performance of the design' ...
-                        ' algorithms, the performance can be the solution quality.'])
-                end
-
-                % the "racing" evaluation method should be used with the "statictic" algorithm comparing method
-                if strcmp(Setting.Evaluate,'racing') && ~strcmp(Setting.Compare,'statistic')
-                    error(['The "racing" evaluation method should be used with the ' ...
-                        'algorithm comparing method of "statistic". '])
-                end
-
-                % should set RacingK when using the "racing" evaluation method
-                if strcmp(Setting.Evaluate,'racing') && isempty(Setting.RacingK)
-                    error(['Please set "Setting.K" as the number of instances evaluated ' ...
-                        'before the first round of racing.'])
-                end
-
-                % should set Surro when using the "approximate" evaluation method
-                if strcmp(Setting.Evaluate,'approximate') && isempty(Setting.Surro)
-                    error(['Please set "Setting.Surro" as the number of exact performance evaluations' ...
-                        ' when using surrogate.'])
-                end
-
-                % it is not necessary to use "statistic" algorithm comparing method when using the "approximate" evaluation method
-                if strcmp(Setting.Evaluate,'approximate') && strcmp(Setting.Compare,'statistic')
-                    error(['It is not necessary to use the "statistic" algorithm ' ...
-                        'comparing method when using the "approximate" evaluation method.'])
-                end
-
-                % should run the design multiple times when using the "statistic" comparsion method
-                if strcmp(Setting.Compare,'statistic') && Setting.AlgRuns == 1
-                    error(['Please run the design multiple times (Setting.AlgRuns>1)' ...
-                        ' when using the "statistic" comparsion method.'])
-                end
-
-                % better to have a large population size when involving the EDA operator
-                if Setting.ProbN < 5 && Setting.AlgP > 1
-                    warning(['It is better to have a large population size if ' ...
-                        'involving the EDA operator'])
-                end
-
-                % AlgP cannot be very large
-                if Setting.AlgQ > 4
-                    warning(['AlgQ is recommended to be larger than 4 for ' ...
-                        'discrete and permutation problems due to the lack' ...
-                        ' of so many search operators'])
-                end
-
-                % better to have a large number of training intrances or have a large number of algorithm runs, in order to make the statistical test discriminative
-                if strcmp(Setting.Compare,'statistic')
-                    warning(['It is better to have a large number of training intrances ' ...
-                        'or have a large number of algorithm runs (set AlgRun to a large number), ' ...
-                        'in order to make the statistical test discriminative.']);
-                end
-
-            case 'solve'
-                % should specify the file or name of the algorithm being used
-                if isempty(Setting.AlgFile) && isempty(Setting.AlgName)
-                    error(['Please specify an algorithm file in Setting.AlgFile or ' ...
-                        'specify an algorithm name in Setting.AlgName.'])
-                end
-
-                % set Tmax and Thres when using runtimeFE or runtimeSec as the performance metric
-                if strcmp(Setting.Metric,'runtimeFE') && isempty(Setting.Tmax)
-                    Setting.Tmax = Setting.ProbFE;
-                end
-                if strcmp(Setting.Metric,'runtimeFE') && isempty(Setting.Thres)
-                    error(['Please set "Setting.Thres" as the lowest acceptable performance ' ...
-                        'of the design algorithms, the performance can be the solution quality.'])
-                end
-                if strcmp(Setting.Metric,'runtimeSec') && isempty(Setting.Tmax)
-                    error('Please set "Setting.Tmax" as the maximum runtime (seconds).')
-                end
-                if strcmp(Setting.Metric,'runtimeSec') && isempty(Setting.Thres)
-                    error(['Please set "Setting.Thres" as the lowest acceptable performance ' ...
-                        'of the design algorithms, the performance can be the solution quality.'])
-                end
-
-                %  time (Tmax) should be function evaluations when using auc
-                if strcmp(Setting.Metric,'auc') && numel(Setting.Tmax) <= 1
-                    error(['"Setting.Tmax" should contain multiple time points. The time ' ...
-                        'points should the numbers of function evaluations spent during ' ...
-                        'the alorithm execution.'])
-                end
-
-                % thresholds should be corresponding to time points when using auc
-                if strcmp(Setting.Metric,'auc') && numel(Setting.Thres) ~= numel(Setting.Tmax)
-                    error(['The number of thresholds in "Setting.Thres" should be equal to ' ...
-                        'the number of time points in "Setting.Tmax". "Setting.Thres" ' ...
-                        'refers to the lowest acceptable performance of the design' ...
-                        ' algorithms, the performance can be the solution quality.'])
-                end
-        end
-        varargout{1} = Setting;
-end
-end
\ No newline at end of file
diff --git a/matlab/Utilities/Others/Output.m b/matlab/Utilities/Others/Output.m
deleted file mode 100644
index 4f8d392..0000000
--- a/matlab/Utilities/Others/Output.m
+++ /dev/null
@@ -1,191 +0,0 @@
-function Output(varargin)
-% Save and output the results.
-
-%----------------------------Copyright-------------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-Setting = varargin{end};
-switch Setting.Mode
-    case 'design'
-        algs          = varargin{1};
-        algTrace      = varargin{2};
-        instanceTrain = varargin{3};
-        instanceTest  = varargin{4};
-        if nargin > 5
-            app = varargin{end-1};
-        end
-
-        % delete previous files
-        delete('Algs.xlsx')
-
-        % save algorithm representations
-        save('Algs.mat','algs');
-
-        %% save final algorithms' pseudocode
-        allCode = cell(50,Setting.AlgN);
-        for i = 1:Setting.AlgN
-            code = pseudocode(algs(i),Setting,i,'final');
-            if length(code) > size(allCode,1)
-                error('Please set the number of rows of "allCode" larger than the length of "code".');
-            end
-            allCode(1:length(code),i) = code;
-            % display on GUI
-            if i == 1 && exist('app','var')
-                app.TextArea2.Value = code;
-            end
-        end
-        writecell(allCode,'Algs.xlsx','Sheet','Final Algs');
-        
-        %% save final algorithms' performance
-        firstRow = cell(1,Setting.AlgN+1);
-        firstRow{1} = 'Instance Index';
-        firstRow{2} = 'Best Algorithm';
-        for i = 2:Setting.AlgN
-            firstRow{i+1} = ['Algorithm ',num2str(i)];
-        end
-        sheetName = 'Perf of Final Algs';
-        writecell(firstRow,'Algs.xlsx','Sheet',sheetName,'Range','A1');
-        
-        firstColumn = repmat(instanceTest',1,Setting.AlgRuns);
-        firstColumn = reshape(firstColumn',length(instanceTest)*Setting.AlgRuns,1);
-        writematrix(firstColumn,'Algs.xlsx','Sheet',sheetName,'Range','A2');
-        
-        performance = zeros(numel(instanceTest)*Setting.AlgRuns,length(algs));
-        for i = 1:length(algs)
-            % reshape algorithm i's all performance values (each run on each instance) to a column vector
-            performance(:,i) = reshape(algs(i).performance(numel(instanceTrain)+1:numel(instanceTrain)+numel(instanceTest),:)',size(performance,1),1);
-        end
-        
-        writematrix(performance,'Algs.xlsx','Sheet',sheetName,'Range','B2');
-
-        %% save pseudocode of the best algorithms found at each iteration of design
-        allCode = cell(50,length(algTrace));
-        for i = 1:length(algTrace)
-            code = pseudocode(algTrace(i),Setting,i,'iterate');
-            if length(code) > size(allCode,1)
-                error('Please set the number of rows of "allCode" larger than the length of "code".');
-            end
-            allCode(1:length(code),i) = code;
-        end
-        writecell(allCode,'Algs.xlsx','Sheet','Best Algs at Iteration');
-
-        %% save performance of the best algorithms found at each iteration of design
-        firstRow = cell(1,length(algTrace)+1);
-        firstRow{1} = 'Instance Index';
-        for i = 1:length(algTrace)
-            firstRow{i+1} = ['Algorithm at Iteration ',num2str(i)];
-        end
-        sheetName = 'Perf of Best Algs at Iteration';
-        writecell(firstRow,'Algs.xlsx','Sheet',sheetName,'Range','A1');
-        
-        firstColumn = repmat(instanceTrain',1,Setting.AlgRuns);
-        firstColumn = reshape(firstColumn',length(instanceTrain)*Setting.AlgRuns,1);
-        writematrix(firstColumn,'Algs.xlsx','Sheet',sheetName,'Range','A2');
-        
-        performTrace = zeros(numel(instanceTrain)*Setting.AlgRuns,length(algTrace));
-        for i = 1:length(algTrace)
-            % reshape algorithm i's all performance values (each run on each instance) to a column vector
-            performTrace(:,i) = reshape(algTrace(i).performance(1:numel(instanceTrain),:)',size(performTrace,1),1);
-        end
-        writematrix(performTrace,'Algs.xlsx','Sheet',sheetName,'Range','B2');
-
-        %% depict the convergence curve of the design process
-        performTrace = mean(performTrace,1);
-        curve = plot(1:length(performTrace),performTrace);
-        xlabel('Iterations');
-        ylabel('Performance');
-        title('Convergence Curve of the Design Process');
-        saveas(curve,'Convergence Curve of the Design Process');
-        close;
-
-    case 'solve'
-        bestSolutions = varargin{1};
-        allSolutions  = varargin{2};
-        instance      = varargin{3};
-
-        % delete previous files
-        delete('Solutions.xlsx')
-        
-        name =  strcat(Setting.AlgName,int2str(Setting.problem_id));
-        % save original format of solutions
-        save(name,'bestSolutions','allSolutions');
-
-        %% save solutions and fitness obtained in the best run on each problem instance
-        numIterations   = size(allSolutions,2);
-        solutions       = cell(1+length(instance),2+numIterations);
-        solutions{1,1}  = 'Instance Index';
-        solutions{1,2}  = 'Best Solution';
-        fitness         = cell(1+length(instance),2+numIterations);
-        fitness{1,1}    = 'Instance Index';
-        fitness{1,2}    = 'Best Solution';
-        constraint      = cell(1+length(instance),2+numIterations);
-        constraint{1,1} = 'Instance Index';
-        constraint{1,2} = 'Best Solution';
-        for i = 1:numIterations
-            solutions{1,2+i}  = ['Iteration ',num2str(i)];
-            fitness{1,2+i}    = ['Iteration ',num2str(i)];
-            constraint{1,2+i} = ['Iteration ',num2str(i)];
-        end
-        for i = 1:length(instance)
-            solutions{1+i,1}  = instance(i);
-            fitness{1+i,1}    = instance(i);
-            constraint{1+i,1} = instance(i);
-            [~,best]          = min(allSolutions(i,:).fits);
-            solutions{1+i,2}  = num2str(allSolutions(i,best).dec);
-            fitness{1+i,2}    = num2str(allSolutions(i,best).fit);
-            constraint{1+i,2} = num2str(allSolutions(i,best).con);
-            for j = 1:numIterations
-                solutions{1+i,2+j}  = num2str(allSolutions(i,j).dec);
-                fitness{1+i,2+j}    = num2str(allSolutions(i,j).fit);
-                constraint{1+i,2+j} = num2str(allSolutions(i,j).con);
-            end
-            % depict convergence curves
-            if i <= 20
-                curve = plot(1:numIterations,allSolutions(i,:).fits);
-                xlabel('Iterations');
-                ylabel('Fitness');
-                title(['Convergence Curve of Instance ',num2str(instance(i))]);
-                saveas(curve,['Convergence Curve of Instance ',num2str(instance(i))]);
-                close;
-            else
-                warning('Only display the first 20 instances for saving computational resources.')
-            end
-        end
-        writecell(solutions,'Solutions.xlsx','Sheet','Solutions','Range','A1');
-        writecell(fitness,'Solutions.xlsx','Sheet','Fitness','Range','A1');
-        writecell(constraint,'Solutions.xlsx','Sheet','Constraint Violation','Range','A1');
-
-        %% save fitness of all runs on each problem instance
-        fitnessAll = cell(1+length(instance),3+Setting.AlgRuns);
-        fitnessAll{1,1} = 'Instance Index';
-        fitnessAll{1,2} = 'Mean';
-        fitnessAll{1,3} = 'Std';
-        for i = 1:Setting.AlgRuns
-            fitnessAll{1,3+i} = ['Run ',num2str(i)];
-        end
-        for i = 1:length(instance)
-            fitnessAll{1+i,1} = instance(i);
-            fitnessAll{1+i,2} = num2str(mean(bestSolutions(i,:).fits));
-            fitnessAll{1+i,3} = num2str(std(bestSolutions(i,:).fits));
-            for j = 1:Setting.AlgRuns
-                fitnessAll{1+i,3+j} = num2str(bestSolutions(i,j).fit);
-            end
-        end
-        writecell(fitnessAll,'Solutions.xlsx','Sheet','Fitness of All Runs','Range','A1');
-end
\ No newline at end of file
diff --git a/matlab/Utilities/Others/Process.m b/matlab/Utilities/Others/Process.m
deleted file mode 100644
index eda2c6c..0000000
--- a/matlab/Utilities/Others/Process.m
+++ /dev/null
@@ -1,119 +0,0 @@
-function [output1,output2] = Process(varargin)
-% The main process of algorithm design and problem solving.
-
-%----------------------------Copyright-------------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the
-% Free Software Foundation, either version 3 of the License, or any later
-% version.
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-if strcmp(varargin{1}(end:-1:end-1),'m.')
-    varargin{1}(end:-1:end-1)=[]; % delete '.m'
-end
-Setting = varargin{end};
-switch Setting.Mode
-    case 'design'
-        tic;
-        str = 'Initializing...';
-        if nargin > 4
-            app = varargin{end-1};
-            app.TextArea.Value = str;
-            drawnow;
-            bar = [];
-        else
-            app = [];
-            bar = waitbar(0,str);
-        end
-        %% construct training problem properties
-        Problem  = struct('name',[],'type',[],'bound',[],'setting',{''},'N',[],'Gmax',[]);
-        instanceTrain = varargin{2};
-        instanceTest  = varargin{3};
-        seedTrain     = randperm(numel(instanceTrain));
-        seedTest      = randperm(numel(instanceTest))+length(seedTrain);
-        instance      = [instanceTrain,instanceTest];
-        for i = 1:numel(instance)
-            Problem(i).name    = varargin{1};
-            Problem(i).setting = '';
-            Problem(i).N       = Setting.ProbN;
-            Problem(i).Gmax    = ceil(Setting.ProbFE/Setting.ProbN);
-            Problem(i).problem_id = Setting.problem_id;
-        end
-        [Problem,Data,~] = feval(str2func(Problem(1).name),Problem,instance,'construct'); % infill problems' constraints and search boundary, construct data properties
-
-
-        %% design algorithms
-        Setting = Space(Problem,Setting); % get design space
-        obj = DESIGN;
-        switch Setting.Generate
-            case 'search' % search algorithms via heuristics
-                [Algs,AlgTrace] = obj.Search(Problem,Data,Setting,seedTrain,app,bar);       
-
-            case 'learn' % learn the algorithm distribution via transformer and reinforcement learning
-                [Algs,AlgTrace] = obj.Learn(Problem,Data,Setting,seedTrain,app,bar);
-            case 'get-p'
-                output1 = obj.Get_performance(Problem,Data,Setting,seedTrain);
-                return;
-        end
-
-        %% test the designed algorithms
-        str = 'Testing... ';
-        if ~isempty(app)
-            app.TextArea.Value = str;
-            drawnow;
-        else
-            waitbar(100,bar,str);
-        end
-        Setting.Evaluate = 'exact';
-        Algs = Algs.Evaluate(Problem,Data,Setting,seedTest);
-        Algs = Algs.Select(Problem,Data,Setting,seedTest); % sort algorithms in descending order in terms of their performance
-        output1 = Algs; % final algorithms
-        output2 = AlgTrace; % best algorithms found at each iteration of design
-
-        str = 'Complete';
-        if ~isempty(app)
-            app.TextArea.Value = str;
-            drawnow;
-        else
-            waitbar(100,bar,str);
-        end
-        toc;
-
-    case 'solve'
-        %% construct problem properties
-        ProblemSolve  = struct('name',[],'type',[],'bound',[],'setting',{''},'N',[],'Gmax',[]);
-        instanceSolve = varargin{2};
-        for i = 1:numel(instanceSolve)
-            ProblemSolve(i).name = varargin{1};
-            ProblemSolve(i).setting = '';
-            ProblemSolve(i).N    = Setting.ProbN;
-            ProblemSolve(i).Gmax = ceil(Setting.ProbFE/Setting.ProbN);
-            ProblemSolve(i).problem_id =Setting.problem_id;    
-        end
-        [ProblemSolve,DataSolve,~] = feval(str2func(ProblemSolve(1).name),ProblemSolve,instanceSolve,'construct'); % infill problems' constraints and search boundary, construct data properties
-
-        %% solve the problem
-        Solution = SOLVE;
-        [Alg,Setting] = Solution.InputAlg(Setting); % algorithm profile
-        if nargin > 3
-            app = varargin{end-1};
-            [bestSolutions,allSolutions] = Solution.RunAlg(Alg,ProblemSolve,DataSolve,app,Setting);
-        else
-            [bestSolutions,allSolutions] = Solution.RunAlg(Alg,ProblemSolve,DataSolve,[],Setting);
-        end
-        output1 = bestSolutions; % the best solution at the final iteration of each algorithm run
-        output2 = allSolutions;  % the best solution at each iteration of the best algorithm run
-end
-end
\ No newline at end of file
diff --git a/matlab/Utilities/Others/pseudocode.m b/matlab/Utilities/Others/pseudocode.m
deleted file mode 100644
index 2e5471f..0000000
--- a/matlab/Utilities/Others/pseudocode.m
+++ /dev/null
@@ -1,219 +0,0 @@
-function Code = pseudocode(algorithm,Setting,algInd,state)
-% Output the pseudocode of the designed algorithm.
-
-%----------------------------Copyright-------------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-% prepare choose
-choose    = algorithm.operatorPheno{1}.Choose;
-choose    = [choose,'('];
-parameter = algorithm.parameterPheno{1}.Choose;
-for j = 1:numel(parameter)
-    temp   = num2str(parameter(j));
-    choose = [choose,temp,','];
-end
-choose = ['  S = ',choose,'S)'];
-
-% prepare update
-update    = algorithm.operatorPheno{1}.Update;
-update    = [update,'('];
-parameter = algorithm.parameterPheno{1}.Update;
-for j = 1:numel(parameter)
-    temp   = num2str(parameter(j));
-    update = [update,temp,','];
-end
-update = ['  S = ',update,'S,S_new)'];
-
-% prepare archive
-if ~isempty(algorithm.operatorPheno{1}.Archive)
-    archive = algorithm.operatorPheno{1}.Archive;
-    archive = ['  A = ',archive,'(A,S)'];
-end
-
-if Setting.AlgP == 1
-    Code = cell(4+7*size(algorithm.operatorPheno{1}.Search,1),1);
-    k = 1;
-    
-    % first row
-    switch state
-        case 'final'
-            if algInd == 1
-                Code{k} = 'Best algorithm:'; k = k+1;
-            else
-                Code{k} =  ['Algorithm ',num2str(algInd),':']; k = k+1;
-            end
-        case 'iterate'
-            Code{k} = ['Algorithm at Iteration ',num2str(algInd),':']; k = k+1;
-    end
-
-    % initialize
-    Code{k} = 'S = initialize()'; k = k+1;
-
-    % archive
-    if ~isempty(algorithm.operatorPheno{1}.Archive)
-        Code{k} = archive; k = k+1;
-    end
-
-    % while
-    Code{k} = 'while algorithm termination condition not met'; k = k+1;
-
-    % for each search operation
-    for j = 1:size(algorithm.operatorPheno{1}.Search,1)
-        % prepare search
-        termninate1 = num2str(algorithm.operatorPheno{1}.Search{j,end}(1));
-        termninate2 = num2str(algorithm.operatorPheno{1}.Search{j,end}(2));
-        search      = algorithm.operatorPheno{1}.Search{j,1};
-        parameter   = algorithm.parameterPheno{1}.Search{j,1};
-        search      = [search,'('];
-        for l = 1:numel(parameter)
-            temp = num2str(parameter(l));
-            search = [search,temp,','];
-        end
-        search = ['  S_new = ',search,'S)'];
-        % if is a sexual evolutonary algorithm with crossover and mutation
-        if ~isempty(algorithm.operatorPheno{1}.Search{j,2})
-            search2    = algorithm.operatorPheno{1}.Search{j,2};
-            parameter2 = algorithm.parameterPheno{1}.Search{j,2};
-            search2    = [search2,'('];
-            for l = 1:numel(parameter2)
-                temp = num2str(parameter2(l));
-                search2 = [search2,temp,','];
-            end
-            search2 = ['  S_new = ',search2,'S_new)'];
-        end
-
-        % search
-        if algorithm.operatorPheno{1}.Search{j,end}(2) == 1 % global search
-            Code{k} = choose; k = k+1;
-            Code{k} = search; k = k+1;
-            if ~isempty(algorithm.operatorPheno{1}.Search{j,2})
-                Code{k} = search2; k = k+1;
-            end
-            Code{k} = update; k = k+1;
-            if ~isempty(algorithm.operatorPheno{1}.Archive)
-                Code{k} = archive; k = k+1;
-            end
-        else % ierative local search
-            Code{k} = ['  while solution improvement>',termninate1,' or inner_iteration<=',termninate2]; k = k+1;
-            Code{k} = ['  ',choose]; k = k+1;
-            Code{k} = ['  ',search]; k = k+1;
-            if ~isempty(algorithm.operatorPheno{1}.Search{j,2})
-                Code{k} = ['  ',search2]; k = k+1;
-            end
-            Code{k} = ['  ',update]; k = k+1;
-            if ~isempty(algorithm.operatorPheno{1}.Archive)
-                Code{k} = ['  ',archive]; k = k+1;
-            end
-            Code{k} = '  end while'; k = k+1;
-        end
-    end
-    Code{k} = 'end while';
-else
-    Code = cell(7+2*Setting.AlgP,1);
-    k = 1;
-
-    % first row
-    switch state
-        case 'final'
-            if algInd == 1
-                Code{k} = 'Best algorithm:'; k = k+1;
-            else
-                Code{k} =  ['Algorithm ',num2str(algInd),':']; k = k+1;
-            end
-        case 'iterate'
-            Code{k} = ['Algorithm at Iteration ',num2str(algInd),':']; k = k+1;
-    end
-
-    % initialize
-    Code{k} = 'S = initialize()'; k = k+1;
-
-    % archive
-    if ~isempty(algorithm.operatorPheno{1}.Archive)
-        Code{k} = archive; k = k+1;
-    end
-
-    % while
-    Code{k} = 'while algorithm termination condition not met'; k = k+1;
-
-    % choose
-    Code{k} = choose; k = k+1;
-
-    % prepare subpopulation
-    dividePop = '  {S1,';
-    for j = 2:Setting.AlgP
-        dividePop = [dividePop,'S',num2str(j),','];
-    end
-    dividePop = [dividePop(1:end-1),'} = S'];
-    mergePop = '  S_new = {S1_new,';
-    for j = 2:Setting.AlgP
-        mergePop = [mergePop,'S',num2str(j),'_new,'];
-    end
-    mergePop = [mergePop(1:end-1),'}'];
-
-    % divide population
-    Code{k} = dividePop; k = k+1;
-
-    % for each search pathway
-    for j = 1:Setting.AlgP
-        % prepare search
-        search      = algorithm.operatorPheno{j}.Search{1};
-        parameter   = algorithm.parameterPheno{j}.Search{1};
-        search      = [search,'('];
-        for l = 1:numel(parameter)
-            temp = num2str(parameter(l));
-            search = [search,temp,','];
-        end
-        search = ['  S',num2str(j),'_new = ',search,'S',num2str(j),')'];
-        % if is a sexual evolutonary algorithm with crossover and mutation
-        if ~isempty(algorithm.operatorPheno{j}.Search{2})
-            search2    = algorithm.operatorPheno{j}.Search{2};
-            parameter2 = algorithm.parameterPheno{j}.Search{2};
-            search2    = [search2,'('];
-            for l = 1:numel(parameter2)
-                temp = num2str(parameter2(l));
-                search2 = [search2,temp,','];
-            end
-            search2 = ['  S',num2str(j),'_new = ',search2,'S',num2str(j),')'];
-        end
-
-        % search
-        Code{k} = search; k = k+1;
-        if ~isempty(algorithm.operatorPheno{j}.Search{2})
-            Code{k} = search2; k = k+1;
-        end
-    end
-
-    % merge subpopulation
-    Code{k} = mergePop; k = k+1;
-
-    % update
-    Code{k} = update; k = k+1;
-
-    % archive
-    if ~isempty(algorithm.operatorPheno{1}.Archive)
-        Code{k} = archive; k = k+1;
-    end
-
-    Code{k} = 'end while';
-end
-
-% delete empty rows
-rowDelete = cellfun(@isempty,Code(:,1));
-Code(rowDelete,:) = [];
-end
\ No newline at end of file
diff --git a/matlab/Utilities/Solutions.xlsx b/matlab/Utilities/Solutions.xlsx
deleted file mode 100644
index 294e586..0000000
Binary files a/matlab/Utilities/Solutions.xlsx and /dev/null differ
diff --git a/matlab/Utilities/Space.m b/matlab/Utilities/Space.m
deleted file mode 100644
index 3acde8f..0000000
--- a/matlab/Utilities/Space.m
+++ /dev/null
@@ -1,84 +0,0 @@
-function Setting = Space(Problem,Setting)
-% Define the design space.
-
-%----------------------------Copyright-------------------------------------
-% Copyright (C) <2023>  <Swarm Intelligence Lab>
-
-% AutoOptLib is a free software. You can use, redistribute, and/or modify
-% it under the terms of the GNU General Public License as published by the 
-% Free Software Foundation, either version 3 of the License, or any later 
-% version. 
-
-% Please reference the paper below if using AutoOptLib in your publication:
-% @article{zhao2023autooptlib,
-%  title={AutoOptLib: A Library of Automatically Designing Metaheuristic 
-%         Optimization Algorithms in Matlab},
-%  author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and 
-%          Yang, Jian and Shi, Yuhui},
-%  journal={arXiv preprint 	arXiv:2303.06536},
-%  year={2023}
-% }
-%--------------------------------------------------------------------------
-
-% operater space
-switch Problem(1).type{1}
-    case 'continuous'
-        Choose  = {'choose_traverse';'choose_tournament';'choose_roulette_wheel';'choose_brainstorm';'choose_nich'}; 
-        Search  = {'search_pso';'search_de_current';'search_de_current_best';'search_de_random';
-            'cross_arithmetic';'cross_sim_binary';'cross_point_one';'cross_point_two';
-            'cross_point_uniform';'search_mu_gaussian';'search_mu_cauchy';'search_mu_polynomial';
-            'search_mu_uniform';'search_eda';'search_cma';'reinit_continuous'};
-        Update  = {'update_greedy';'update_round_robin';'update_pairwise';'update_always';'update_simulated_annealing'};
-       
-    case 'discrete'
-        Choose  = {'choose_traverse';'choose_tournament';'choose_roulette_wheel';'choose_nich'};
-        Search  = {'cross_point_one';'cross_point_two';'cross_point_n';'cross_point_uniform';'search_reset_one';
-            'search_reset_n';'search_reset_rand';'reinit_discrete'};
-        Update  = {'update_greedy';'update_round_robin';'update_pairwise';'update_always';'update_simulated_annealing'};
-
-    case 'permutation'
-        Choose  = {'choose_traverse';'choose_tournament';'choose_roulette_wheel';'choose_nich'};
-        Search  = {'cross_order_two';'cross_order_n';'search_swap';'search_swap_multi';
-            'search_scramble';'search_insert';'reinit_permutation'};
-        Update  = {'update_greedy';'update_round_robin';'update_pairwise';'update_always';'update_simulated_annealing'};
-end
-Setting.TunePara = false; % true/false, turn to true if perform hyperparameter configuration
-
-OpSpace = [1,length(Choose);
-    length(Choose)+1,length(Choose)+length(Search);
-    length(Choose)+length(Search)+1,length(Choose)+length(Search)+length(Update)]; % each row reports the search space of one kind of operaters, e.g., the search space of Choose is intergers from 1 to 4.
-AllOp = [Choose;Search;Update];
-
-% parameter and behavior space
-ParaSpace      = cell(length(AllOp),1); % parameter space
-ParaLocalSpace = cell(length(AllOp),1); % parameter space for performing local search
-BehavSpace     = cell(length(AllOp),1); % search behaviors
-for i = 1:length(AllOp)
-    [thisParaSpace,~]  = feval(str2func(AllOp{i}),Problem,'parameter');
-    [thisBehavSpace,~] = feval(str2func(AllOp{i}),'behavior');
-    ParaSpace{i} = thisParaSpace;
-    BehavSpace{i} = thisBehavSpace;
-    if ~isempty(thisBehavSpace{1,1}) && ~isempty(thisParaSpace) % if the operator can perform local search and has parameter(s)
-        if isempty(thisBehavSpace{2,1}) % if only performs local search
-            thisParaLocalSpace = thisParaSpace;
-        else
-            thisParaLocalSpace = zeros(size(thisParaSpace));
-            for j = 1:size(thisParaLocalSpace,1) % for each parameter of operator i
-                lower = thisParaSpace(j,1);
-                upper = thisParaSpace(j,2);
-                trend = thisBehavSpace{1,j+1}; % parameter setting for local search
-                if strcmp(trend,'small')
-                    thisParaLocalSpace(j,:) = [lower,lower+(upper-lower)*Setting.LSRange];
-                elseif strcmp(trend,'large')
-                    thisParaLocalSpace(j,:) = [upper-upper*Setting.LSRange,upper];
-                end
-            end
-        end
-        ParaLocalSpace{i} = thisParaLocalSpace;
-    end
-end
-Setting.OpSpace        = OpSpace;
-Setting.AllOp          = AllOp;
-Setting.ParaSpace      = ParaSpace;
-Setting.ParaLocalSpace = ParaLocalSpace;
-Setting.BehavSpace     = BehavSpace;
\ No newline at end of file
diff --git a/matlab/__pycache__/IOH_Test.cpython-36.pyc b/matlab/__pycache__/IOH_Test.cpython-36.pyc
deleted file mode 100644
index 3789ecb..0000000
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diff --git a/matlab/__pycache__/IOH_Test.cpython-38.pyc b/matlab/__pycache__/IOH_Test.cpython-38.pyc
deleted file mode 100644
index db02938..0000000
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diff --git a/matlab/__pycache__/IOH_Test.cpython-39.pyc b/matlab/__pycache__/IOH_Test.cpython-39.pyc
deleted file mode 100644
index e5d7902..0000000
Binary files a/matlab/__pycache__/IOH_Test.cpython-39.pyc and /dev/null differ
diff --git a/matlab/eval_alg.m b/matlab/eval_alg.m
deleted file mode 100644
index 51167b0..0000000
--- a/matlab/eval_alg.m
+++ /dev/null
@@ -1,205 +0,0 @@
-exact_datas_from_ArchSolution();
-function  run_compare_alg()
-alglist = ["Discrete Genetic Algorithm","Discrete Iterative Local Search","Discrete Simulated Annealing"];
-alglist = ["Discrete Simulated Annealing"];
-for instance =1:3
- for i = 1:numel(alglist)
-     media_res=[];
-     for j = 2:23
-        AutoOpt('Mode','solve','Problem','pbo','InstanceSolve',[4],'AlgName',alglist(i),'ProbN',50000,'ProbFE',50,'AlgRuns',30,'problem_id',j);
-        res =[];
-        load(strcat(alglist(i),int2str(j)));
-        [x, y] = size(bestSolutions);
-        for ii =1: y
-            res(ii)=cat(1,bestSolutions(1,ii).fit);
-        end
-        path = strcat('result/',strrep(alglist(i), ' ', '_'),'/instance_',int2str(instance),'/');
-        % 确保路径存在
-        if ~exist(path, 'dir')
-            mkdir(path);
-            addpath(genpath('result'));
-        end
-        fullpath = strcat(path,'f',int2str(j));
-        save(fullpath,'res')
-        disp(fullpath);
-        media_res(j)=median(res);
-     end
- end
-end
-end
- 
-function  cal_compare_mean_var()
- alglist = ["Discrete Genetic Algorithm", "Discrete Iterative Local Search","Discrete Simulated Annealing"];
-
- for instance = 1:3
-     for i = 1:numel(alglist)
-          total_mean = [];
-          total_var = [];
-         for j = 1:23
-            path = strcat('result/',strrep(alglist(i), ' ', '_'),'/instance_',int2str(instance),'/');
-            fullpath = strcat(path,'f',int2str(j));
-            load(fullpath);
-            total_mean(1,j) = -mean(res); %performance为了最小化问题取反，这里取反为正常值
-            total_var(1,j) = var(res);
-         end
-              % 写入均值矩阵到 Excel 文件的第一个工作表
-        filename = strcat(path,'mean_var_design_data.xlsx'); % Excel 文件名
-        mean_sheet = 'Mean'; % 均值工作表名称
-        xlswrite(filename, total_mean, mean_sheet);
-
-        % 写入方差矩阵到 Excel 文件的第二个工作表
-        var_sheet = 'Variance'; % 方差工作表名称
-        xlswrite(filename, total_var, var_sheet);
-     end
-end
-
-end
- 
-function  run_design_alg()
-B{1}=[2,13,7,24,9,18,8];
-B{2}=[2,13,4,9,19];
-B{3}=[1,12,7,26,8];
-B{4}=[2,11,4,9,18,8];
-B{5}=[1,12,5,8];
-B{6}=[0,12,7,25,8];
-B{7}=[0,11,5,8];
-B{8}=[2,11,5,8];
-B{9}=[3,11,7,24,8];
-B{10}=[1,13,7,24,8];
-B{11}=[2,11,7,18,9,18];
-B{12}=[2,13,7,18,9,18];
-B{13}=[2,14,5,9,18];
-B{14}=[1,13,7,23,9,20];
-B{15}=[1,13,5,8,9,20];
-B{16}=[2,14,7,19,9,19];
-B{17}=[0,11,6,21,9,20];
-B{18}=[2,14,4,8];
-B{19}=[2,14,4,8];
-B{20}=[2,12,7,26,9,18,8];
-B{21}=[2,12,5,8];
-B{22}=[0,12,7,25,8];
-B{23}=[1,11,7,21,8];
-
-%for another run:
-% B{2}=[2, 15, 18,  7, 18,  9, 18];
-% B{3}=[2, 13,  8,  4,  9, 18];
-% B{4}=[ 2, 11, 10,  7, 19,  8];
-% B{5}=[2, 15, 24,  4,  8];
-% B{6}=[1, 11,  7, 26,  8];
-% B{7}=[2, 13,  8,  5,  9, 18,];
-% 
-% B{8}=[2, 11,  5,  8];
-% B{9}=[ 2, 13,  9, 18,  5,  8];
-% B{10}=[1, 15, 21,  7, 23,  8];
-% B{11}=[2, 11,  9, 18,  4,  8];
-% B{12}=[2, 15, 22,  4,  9, 18];
-% B{13}=[2, 14,  5,  9, 18];
-% B{14}=[1, 11,  7, 25,  9, 20];
-% B{15}=[2, 11,  5,  9, 19];
-% B{16}=[ 0, 12,  7, 24,  9, 19];
-% B{17}=[3, 15, 19,  5,  8,  9, 20];
-% B{18}=[2, 15, 25,  5,  9, 19,  8];
-% B{19}=[2, 14,  5,  8];
-% B{20}=[2, 11,  9, 21,  7, 20];
-% B{21}=[2, 11,  7, 26,  8];
-% B{22}=[1, 12,  5,  8];
-% B{23}=[1, 12,  7, 24,  8];
-
-for j = 4 %1:3
-        indices = [1, 13, 15, 20];
-     for i = indices % 1:23
-        info = ['instance:',int2str(j),' problem:',int2str(i)];
-        disp(info)
-        %datestr(now())
-        [performance, change,solution] = get_per(B{i},i,[j],1);
-        continue;
-        path = ['result/design/instance_',int2str(j),'/'];
-        % 确保路径存在
-        if ~exist(path, 'dir')
-            mkdir(path);
-            addpath(genpath('result'));
-        end
-        fullpath = [path,'f',int2str(i)];
-        res = performance(1,:);
-        % save original format of solutions
-        save(fullpath,'res');
-        %datestr(now())
-     end
-end
-end
- 
-function  cal_design_mean_var()
-total_mean = [];
-total_var = [];
-for i = 1:3
-    for j = 1:23
-        path = ['result/design/instance_',int2str(i),'/f',int2str(j)];
-        load(path);
-        total_mean(1,j) = -mean(res);
-        total_var(1,j) = var(res);
-    end
-
-    % 写入均值矩阵到 Excel 文件的第一个工作表
-    filename = ['result/design/instance_',int2str(i),'/mean_var_design_data.xlsx']; % Excel 文件名
-    mean_sheet = 'Mean'; % 均值工作表名称
-    xlswrite(filename, total_mean, mean_sheet);
-
-    % 写入方差矩阵到 Excel 文件的第二个工作表
-    var_sheet = 'Variance'; % 方差工作表名称
-    xlswrite(filename, total_var, var_sheet);
-    end
-end
-
-function  run_train_in_one_design_alg()
-B{1}=[1,11,7,26,8];
-B{2}=[2,13,7,26,8,9,18];
-B{3}=[1,11,7,26,8];
-B{4}=[1,11,7,26,8];
-B{5}=[1,11,7,26,8];
-B{6}=[1,11,7,26,8];
-B{7}=[1,11,7,26,8];
-B{8}=[1,11,7,26,8];
-B{9}=[3,11,7,24,8];
-B{10}=[1,13,7,24,8];
-B{11}=[2,11,7,18,9,18];
-B{12}=[2,13,7,18,9,18];
-B{13}=[2,14,5,9,18];
-B{14}=[1,13,7,23,9,20];
-B{15}=[1,13,5,8,9,20];
-B{16}=[2,14,7,19,9,19];
-B{17}=[0,11,6,21,9,20];
-B{18}=[2,14,4,8];
-B{19}=[2,14,4,8];
-B{20}=[2,12,7,26,9,18,8];
-B{21}=[2,12,5,8];
-B{22}=[0,12,7,25,8];
-B{23}=[1,11,7,21,8];
- for i = 1:1:8
-    i
-    datestr(now())
-    [performance, change,solution] = get_per(B{i},i,[4],1);
-    name = 'result/train_in_one'+int2str(i);
-    res = performance(1,:);
-    % save original format of solutions
-    save(name,'res');
-    datestr(now())
- end
-
- end
-
-function run_autoopt_for_pbo()
-AutoOpt('Mode','design','Problem','pbo','InstanceTrain',[1,2,3],'InstanceTest',4,'problem_id',1)
-end
- 
-function exact_datas_from_ArchSolution()
-    data_path = '../draw/datas/mats/ArchSolution/';
-    problem_set = [1,13,15,20]
-    for problem = problem_set
-        path = [data_path,'ArchSolution',int2str(problem),'.mat']
-        load(path);
-        res = [ArchSolution(1,:).fit];
-        save([data_path,'f',int2str(problem)],'res')
-    end
-    
-end
- 
diff --git a/matlab/get_perf.m b/matlab/get_perf.m
deleted file mode 100644
index a2c6666..0000000
--- a/matlab/get_perf.m
+++ /dev/null
@@ -1,122 +0,0 @@
-function performance = get_perf(seq,problem_id,instanceTrain,eval)
-% get the performance of the designed algorithm
-
-addpath(genpath('Utilities'));
-addpath(genpath('Problems'));
-addpath(genpath('Main'));
-addpath(genpath('Components'));
-addpath(genpath('C:\Program Files\MATLAB\R2020b\toolbox\matpower7.1'));
-warning('off')
-
-% seq = [1.0,29.0, 9.0,19.0,29.0, 12.0,29.0]; % simple one search operator
-% seq = [2.0,29.0, 5.0,29.0, 10.0,20.0,29.0, 13.0,29.0]; % simple GA
-% seq = [3.0,31.0,21.0, 8.0,29.0, 11.0,29.0, 14.0,29.0]; % with fork
-% seq = [0.0,29.0, 8.0,30.0,21.0, 11.0,29.0, 16.0,19.0,29.0]; % with iterate
-% seq = [3.0,31.0,20.0,8.0,29.0,12.0,29.0]; 
-
-% instanceTrain = 1;
-% problem_id = 0;
-% eval = 0;
-
-% setting
-problem      = 'pbo'; % target problem, pbo/beamforming/blackstart
-seq_max_comp = 6; % maximun number of components in a sequence
-num_comp     = 17; % number of candidate components
-other_token  = {'begin';'end';1;2;3;4;5;6;7;8;9;10;'forward';'iterate';'fork'}; % tokens that not involved in the matlab AutoOpt software
-values       = {0;0;0;0; 0;0;1;1;0;1;1;0; 0;0;0;0;1; 0;0;0;0;0;0;0;0;0;0;0;0; 0;1;1}; % number of hyperparameters of each token
-dict_conds   = [0.01,0.05,0.1,0.15,0.2]; % candidate count conditions
-dict_forks   = 1:seq_max_comp; % hyperparameters of fork pointer
-
-% prepare
-switch problem
-    case 'pbo'
-        [Problem,Data,Setting,seedTrain] = opt_env('Mode','design','Problem','pbo','InstanceTrain',instanceTrain,'InstanceTest',1,'Problem_id',problem_id,'eval',eval);
-    case 'beamforming'
-        [Problem,Data,Setting,seedTrain] = opt_env('Mode','design','Problem','beamforming','InstanceTrain',instanceTrain,'InstanceTest',3,'Problem_id',problem_id,'eval',eval);
-    case 'blackstart'
-        [Problem,Data,Setting,seedTrain] = opt_env('Mode','design','Problem','blackstart','InstanceTrain',instanceTrain,'InstanceTest',[],'Problem_id',problem_id,'eval',eval);
-end
-seq  = seq + 1; % python list from 0, add 1 in matlab
-keys = [Setting.AllOp;other_token]; % all tokens
-dict = [keys,values]; % for decoding the sequence representation of the designed algorithm
-
-% decode the sequence
-indOp     = []; % indexes of components
-Paras     = cell(num_comp,2); % 17 components' hyperparameters
-Conds     = zeros(num_comp,1); % 17 components, each has 1 condition
-ParaSpace = Setting.ParaSpace;
-indPtr    = []; % indexes of pointers
-paraPtr   = []; % conditions associated with the iterate pointer or hyperparameter of the fork pointer
-i = 1;
-while i <= length(seq)
-    thisToken = seq(i);
-    numPara   = dict{thisToken,2};  % number of hyperparameters of this token
-
-    if thisToken <= num_comp % if component
-        indOp = [indOp,thisToken];
-        if numPara > 0
-            tempPara = zeros(numel(numPara),1);
-            for j = 1:numel(numPara)
-                para = dict{seq(i+j),1}; % the next token
-                tempPara(j) = ParaSpace{thisToken}(:,1) + (ParaSpace{thisToken}(:,2)-ParaSpace{thisToken}(:,1))/9.*(para-1); % min+(max-min)/9*(para-1)
-            end
-            Paras{thisToken,1} = tempPara;
-            i = i+1+numel(numPara);
-        else
-            i = i+1;
-        end
-    elseif thisToken > size(keys,1)-3 % if pointer
-        indPtr  = [indPtr,thisToken];
-        if numPara == 1 % iterate or fork pointer
-            para = dict{seq(i+1),1}; 
-            if thisToken == size(keys,1)-1 % if iterate pointer, para is the count condition
-                para = dict_conds(para);
-            elseif thisToken == size(keys,1) % if fork pointer, para is the hyperparameter of fork
-                para = dict_forks(para) - 1; % dict_forks(para);
-            end
-            paraPtr = [paraPtr,para];
-            i = i+2;
-        elseif numPara == 0 % forward pointer
-            paraPtr = [paraPtr,0];
-            i = i+1;
-        end
-    end
-end
-
-indFork = find(indPtr==32); % indexes (in indOp) of components that are associated with the fork pointer
-Setting.AlgP = numel(indFork)+1; % number of branches determined by the fork pointer
-Operators = cell(1,Setting.AlgP);
-for i = 1:Setting.AlgP
-    % for the fork pointer and its hyperparameter
-    if i == 1 % the first branch (whole sequence)
-        indOpTemp = indOp;
-    else % sequence segments determined by fork
-        paraPtr(indFork(i-1)) = min(paraPtr(indFork(i-1)),length(indOp));
-        indOpTemp = [indOp(1:indFork(i-1)),indOp(paraPtr(indFork(i-1)):end)];
-    end
-    
-    opAdjMat = []; % operators' graph representation (adjacent matrix) to fit the AutoOpt software
-    for j = 1:length(indOpTemp)-1
-        opAdjMat = [opAdjMat;indOpTemp(1,j:j+1)]; 
-    end
-    Operators{i} = opAdjMat;
-    
-    % for the forward and iterate pointers and the count condition
-    indSearch = indOpTemp(2:end-1); % indexes of search operators
-    for k = 1:numel(indSearch)
-        if indPtr(k+1) == size(keys,1)-1 % if the search component is associated with the iterate pointer
-            Paras{indSearch(k),2} = 'LS'; % the search component iterate
-            Conds(indSearch(k)) = paraPtr(k+1)*Setting.ProbFE;
-        elseif indPtr(k+1) == size(keys,1)-2 % if the search component is associated with the forward pointer
-            Paras{indSearch(k),2} = 'GS'; % the search component forward
-        end
-    end
-end
-Setting.Conds = Conds;
-
-% evaluate the designed algorithm by the AutoOpt software
-Alg = DESIGN;
-Alg = Alg.Construct2(Operators,Paras,Problem,Setting);
-[Alg,~]  = Alg.Evaluate(Problem,Data,Setting,seedTrain);
-performance = Alg.performance;
-end
\ No newline at end of file
diff --git a/matlab/irace_sa.py b/matlab/irace_sa.py
deleted file mode 100644
index 5630ff8..0000000
--- a/matlab/irace_sa.py
+++ /dev/null
@@ -1,218 +0,0 @@
-import os
-import ioh
-import numpy
-import numpy as np
-import array
-import random
-import pandas as pd
-from irace import irace
-import math
-import matplotlib.pyplot as plt
-import socket
-import pickle
-
-sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
-# 设置接收数据的超时时间为 5 秒
-sock.settimeout(600)
-    # 连接到服务器
-server_address = ('192.168.1.109', 30000)
-sock.connect(server_address)
-
-const_problem_id = 1 
-const_problem_dim = 1 # for matlab dim 1,2,3 for train,4 for test [100 225 400 625] 
-dim_to_normal = [100, 225, 400, 625] 
-const_FE = 50000
-
-random.seed(0)
-
-parameters_table = '''
-T         "" r (0.01, 0.05)
-'''
-
-default_values = '''
-T           
-0.03           
-'''
-
-# These are dummy "instances", we are tuning only on a single function.
-instances = np.arange(100)
-
-# See https://mlopez-ibanez.github.io/irace/reference/defaultScenario.html
-scenario = dict(
-    instances = instances,
-    maxExperiments = 200,
-    debugLevel = 3,
-    digits = 5,
-    parallel=1, # It can run in parallel ! 
-    logFile = "")
-        
-    
-def socket_sa(T):
-    try:
-        message = f"{T},{const_problem_id},{const_problem_dim}"
-        # 发送指令给 MATLAB 服务器
-        sock.sendall(message.encode())
-
-        # 等待并接收响应
-        data = sock.recv(1024)
-        print('Received:', data.decode())
-
-    except socket.timeout:
-        print('Socket timed out while waiting for response')
-    return float(data.decode())
-
-def sa_pbo(x0, length, problem_id):
-    #print("test")
-    #print(int(problem_id))
-    # In order to instantiate a problem instance, we can do the following:
-    problem = ioh.get_problem(
-        int(problem_id),
-        instance=1,
-        dimension=int(length),
-        problem_class=ioh.ProblemClass.PBO
-    )
-
-    problem.enforce_bounds(how=ioh.ConstraintEnforcement.SOFT, weight=1.0, exponent=1.0)
-
-    # We can access the contraint information of the problem
-    population = np.array(x0).astype(int)
-    #print(population)
-    # Evaluation happens like a 'normal' objective function would
-    res = problem(population)
-    neg_res = [-x for x in res]
-    return neg_res
-
-
-def sa(problem_id,dim,problem_fe,T):
-    problem_fe = 50000
-    prob_N = 50
-    Tf = 0.01
-    Gmax = problem_fe/prob_N
-
-    cur_problem_fe = 0
-    # calculate iters
-    alpha  = (Tf/T)**(1/Gmax)
-    now_x = []
-    for i in range(prob_N):
-        x = np.random.randint(0,2,dim)
-        now_x.append(x)
-    best = 0
-    current_fe = 0
-    G = 0
-    record = [] # record each iter best
-    record_new = [] # record each iter best
-    # annealing
-    while T > Tf and cur_problem_fe < problem_fe:
-        # iteration
-        f_now = sa_pbo(now_x, dim, problem_id)
-        record.append(min(f_now))
-        new_x = get_new_x(now_x)
-        f_new = sa_pbo(new_x, dim, problem_id)
-        record_new.append(min(f_new))        
-        accept = Metrospolis(T,f_now, f_new)
-
-        for i in range(len(accept)):
-            if random.random() < accept[i]:
-                now_x[i]=new_x[i]
-        if min(f_new)<best:
-            best = min(f_new)
-            #print(f'G:{G},best:{best}')
-
-        # cooling
-        T = T * alpha
-        current_fe = current_fe + len(now_x)
-        G = G+1
-
-    # plt.plot(record)
-    # plt.plot(record_new)
-    # plt.savefig('plot.png')
-    print(best)
-    return best 
-
-def Metrospolis(T, f_now, f_new):
-    accept = []
-    for i in range(len(f_now)):
-        if f_new[i] < f_now[i]:
-            accept.append(1)
-        else:
-            p = math.exp((f_now[i]-f_new[i]) /abs(f_now[i]+1e-6) / T)
-            accept.append(p)
-    
-    return accept
-def get_new_x(X0):
-    X_temp = X0.copy()
-    for i in range(len(X0)):
-        random_integer = random.randint(0, X0[i].shape[0]-1)
-        X_temp[i][random_integer] = int(not(X_temp[i][random_integer]))
-    return X_temp
-
-    
-
-# test
-def test_default_set():
-    df = pd.DataFrame(columns=['Problem', 'dim', 'Mean', 'Variance'])
-    for _problem_id in range(1,24):
-        total_res=[]
-        for epoch in range(30):
-            res = sa(_problem_id,625,const_FE,0.1)
-            total_res.append(res)    
-
-        means = np.mean(total_res)
-        variances = np.var(total_res)  
-        df = df._append({
-        'Problem': _problem_id,
-        'dim': 625,
-        'Mean': means,
-        'Variance': variances
-        }, ignore_index=True)
-    return
-
-
-# This target_runner is over-complicated on purpose to show what is possible.
-def target_runner(experiment, scenario):
-    if scenario['debugLevel'] > 0:
-        # Some configurations produced a warning, but the values are within the limits. That seems a bug in scipy. TODO: Report the bug to scipy.
-        print(f'{experiment["configuration"]}')
-        pass
-    #res = sa(const_problem_id,const_problem_dim,const_FE,**experiment['configuration'])
-    res = socket_sa(**experiment['configuration'])
-    return dict(cost=res)
-
-    
-
-def run_irace():
-    tuner = irace(scenario, parameters_table, target_runner)
-    tuner.set_initial_from_str(default_values)
-    best_confs = tuner.run()
-    total_res = []
-    print(f'best_cofs:{best_confs}')
-    
-    for epoch in range(30):
-        res = socket_sa(best_confs['T'][0])
-        #res = sa(const_problem_id,const_problem_dim,const_FE,best_confs['T'][0])
-        total_res.append(res)
-    return total_res
-
-#df = pd.read_csv('/home/booze/code/ALDes/sa_results.csv')          
-df = pd.DataFrame(columns=['Problem', 'dim', 'Mean', 'Variance'])
-df.to_csv('matlab/result/sa_irace/instance_4/result.csv', index=False)
-for _dim in [4]:
-    for _problem_id in range(1,24):
-        const_problem_id = _problem_id
-        const_problem_dim = _dim
-        res = run_irace()
-        dump_file = f'matlab/result/sa_irace/instance_4/f{_problem_id}.pkl'
-        with open(dump_file, 'wb') as f:
-            # 使用pickle.dump()将字典对象序列化并保存到文件中
-            pickle.dump(res, f)
-        means = np.mean(res)
-        variances = np.var(res)
-        df = df._append({
-            'Problem': _problem_id,
-            'dim': dim_to_normal[_dim-1],
-            'Mean': means,
-            'Variance': variances
-        }, ignore_index=True)
-        df.to_csv('matlab/result/sa_irace/instance_4/result.csv', index=False)
-
-
diff --git a/matlab/irace_tabu.py b/matlab/irace_tabu.py
deleted file mode 100644
index 3fef8ad..0000000
--- a/matlab/irace_tabu.py
+++ /dev/null
@@ -1,188 +0,0 @@
-import os
-import ioh
-import numpy as np
-import array
-
-import pandas as pd
-from irace import irace
-import pickle
-
-const_problem_id = 1 
-const_problem_dim = 100
-const_FE = 50000
-def write_excel(res,shell):
-    res = res.reshape(1,-1)
-    # 将 NumPy 数组转换为 pandas 数据框架
-    df = pd.DataFrame(res)
-
-    # 打开现有的 Excel 文件
-    excel_file = 'D:/01Code/transformer-rl-3/mean_var_data.xlsx'
-    existing_data = pd.read_excel(excel_file)
-
-    # 将新数据追加到现有数据后面
-    with pd.ExcelWriter(excel_file, engine='openpyxl', mode='a') as writer:
-        df.to_excel(writer, sheet_name=shell, index=False,header=False)
-        
-def tabu_pbo(x0, length, problem_id):
-    #print("test")
-    #print(int(problem_id))
-    # In order to instantiate a problem instance, we can do the following:
-    problem = ioh.get_problem(
-        int(problem_id),
-        instance=1,
-        dimension=int(length),
-        problem_class=ioh.ProblemClass.PBO
-    )
-
-    problem.enforce_bounds(how=ioh.ConstraintEnforcement.SOFT, weight=1.0, exponent=1.0)
-
-    # We can access the contraint information of the problem
-    population = np.array(x0).astype(int)
-    #print(population)
-    # Evaluation happens like a 'normal' objective function would
-    res = problem(population)
-    return res
-
-def tabu_search(problem_id,dim,problem_fe,tabu_list_length,tabu_list_cycle):
-    tabu_list_length = int(tabu_list_length)
-    tabu_list_cycle = int(tabu_list_cycle)
-    best = np.random.randint(0,2,dim)
-    best_fit = tabu_pbo(best, dim, problem_id)
-    cur_problem_fe = 0
-    tabu_list =  [0] * dim
-    tabu_list = np.array(tabu_list)
-    while(cur_problem_fe<=problem_fe):
-        neighbors = build_neighbors(best,tabu_list)
-        fitness = tabu_pbo(neighbors, dim, problem_id)
-        if max(fitness) > best_fit:
-            best_fit = max(fitness)
-            best = neighbors[fitness.index(best_fit)]
-            
-        for i in range(len(tabu_list)):
-            if tabu_list[i]>0:
-                tabu_list[i] -= 1
-        #计算禁忌的个数
-        count_nonzero = np.count_nonzero(tabu_list)
-        if count_nonzero==tabu_list_length:#禁忌表已满，减去最早加入的那个
-            non_zero_elements = tabu_list[tabu_list!=0]
-            min_non_zero_value = np.min(non_zero_elements)
-            # 找到不为零的最小值的索引
-            min_non_zero_index = np.where(tabu_list == min_non_zero_value)[0][0]
-            # 将该元素置为零
-            tabu_list[min_non_zero_index] = 0
-        #加入最好邻域解对应的tabu
-        tabu_list[fitness.index(max(fitness))] = tabu_list_cycle
-        cur_problem_fe = cur_problem_fe + len(neighbors)
-    print('problem{1},dim {2},best fitness {0} '.format(best_fit,problem_id,dim))
-    return  best_fit
-
-def build_neighbors(X0,tabu_list):
-    neighbors = []
-    for i in range(X0.shape[0]):
-        if tabu_list[i]==0:
-            X_temp = X0.copy()
-            X_temp[i] = int(not(X_temp[i]))
-            neighbors.append(X_temp)
-    return neighbors
-
-def run_tabu():
-    res = np.zeros((30, 23))
-    for j in range(30):
-        for i in range(1,24):
-            res[j,i-1] = tabu_search(i,625,50000,625,3)
-
-    column_means = np.mean(res, axis=0)
-
-    # 计算每行的方差
-    column_variances = np.var(res, axis=0)
-    write_excel(column_means,'Tabu_mean')
-    write_excel(column_variances, 'Tabu_var')
-    print("tsa")
-    
-    
-# This target_runner is over-complicated on purpose to show what is possible.
-def target_runner(experiment, scenario):
-    if scenario['debugLevel'] > 0:
-        # Some configurations produced a warning, but the values are within the limits. That seems a bug in scipy. TODO: Report the bug to scipy.
-        print(f'{experiment["configuration"]}')
-    res = tabu_search(const_problem_id,const_problem_dim,const_FE,**experiment['configuration'])
-
-    return dict(cost=-res)
-
-    
-parameters_table = '''
-tabu_list_length       "" i (1, 625)
-tabu_list_cycle        "" i (1, 100) 
-'''
-
-default_values = '''
-tabu_list_length tabu_list_cycle 
-10               3            
-'''
-
-# These are dummy "instances", we are tuning only on a single function.
-instances = np.arange(100)
-
-# See https://mlopez-ibanez.github.io/irace/reference/defaultScenario.html
-scenario = dict(
-    instances = instances,
-    maxExperiments = 100,
-    debugLevel = 3,
-    digits = 5,
-    parallel=10, # It can run in parallel ! 
-    logFile = "")
-
-def run_irace():
-    tuner = irace(scenario, parameters_table, target_runner)
-    tuner.set_initial_from_str(default_values)
-    best_confs = tuner.run()
-    
-    total_res = []
-    # Pandas DataFrame
-    print(best_confs)
-    #for i in range(len(best_confs)):
-    #only use the first 
-    #print(f'tabu_list_length:{best_confs['tabu_list_length'][0]}, tabu_list_cycle:{best_confs['tabu_list_cycle'][0]}')
-    for epoch in range(30):
-        res = tabu_search(const_problem_id,const_problem_dim,const_FE,best_confs['tabu_list_length'][0],best_confs['tabu_list_cycle'][0])
-        total_res.append(res)
-    return total_res
-
-
-def write_excel(res,shell,path):
-    # 将 NumPy 数组转换为 pandas 数据框架
-    df = pd.DataFrame(res)
-
-    # 打开现有的 Excel 文件
-    existing_data = pd.read_excel(path)
-
-    # 将新数据追加到现有数据后面
-    with pd.ExcelWriter(path, engine='openpyxl', mode='a') as writer:
-        df.to_excel(writer, sheet_name=shell, index=False,header=False)            
-            
-df = pd.DataFrame(columns=['Problem', 'dim', 'Mean', 'Variance'])
-df.to_csv('../matlab/result/tabu_irace/instance_4/result.csv', index=False)
-#df = pd.read_csv('/home/booze/code/ALDes/tabu_result.csv')     
-for _dim in [625]:
-    for _problem_id in range(1,24):
-        const_problem_dim = _dim
-        const_problem_id = _problem_id
-        print(f'current problem dim,id: {const_problem_dim} , {const_problem_id}')
-        res = run_irace()
-
-        dump_file = f'../matlab/result/tabu_irace/instance_4/f{_problem_id}.pkl'
-        with open(dump_file, 'wb') as f:
-            # 使用pickle.dump()将字典对象序列化并保存到文件中
-            pickle.dump(res, f)
-
-        means = np.mean(res)
-        variances = np.var(res)
-
-        df = df._append({
-            'Problem': _problem_id,
-            'dim': _dim,
-            'Mean': means,
-            'Variance': variances
-        }, ignore_index=True)
-
-        df.to_csv('../matlab/result/tabu_irace/instance_4/result.csv', index=False)
\ No newline at end of file
diff --git a/matlab/python_matlab.py b/matlab/python_matlab.py
deleted file mode 100644
index 8fc10b9..0000000
--- a/matlab/python_matlab.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import matlab.engine
-import matlab
-import os
-
-eng = matlab.engine.start_matlab()
-# 此地址为test.m文件存放的地址
-work_path = os.getcwd() + "\\matlab"
-eng.cd(work_path)
-
-a = matlab.double([0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5])
-
-#c = eng.get_per(a)
-result = eng.sa_for_python()
-print(result)
\ No newline at end of file
diff --git a/matlab/sa_socket.m b/matlab/sa_socket.m
deleted file mode 100644
index a913e35..0000000
--- a/matlab/sa_socket.m
+++ /dev/null
@@ -1,60 +0,0 @@
-try
-    % 创建一个 TCP/IP 服务器对象，监听所有 IP 地址上的端口 30000
-    t = tcpip('0.0.0.0', 30000, 'NetworkRole', 'server');
-    t.Timeout = 10;  % 设置超时时间为 10 秒
-
-    % 打开 TCP/IP 连接，等待客户端连接
-    fopen(t);
-    disp('MATLAB server is waiting for connection...');
-
-    % 无限循环以处理客户端请求
-    while true
-        % 检查是否有可用数据
-        if t.BytesAvailable > 0
-            % 从连接中读取所有可用数据
-            data = fread(t, t.BytesAvailable);
-            
-            % 将读取的数据转换为字符串
-            command = char(data');
-            % 解析接收到的整数
-            numbers = str2double(strsplit(command, ','));
-            T = numbers(1);
-            problem_id = numbers(2);
-            problem_dim = numbers(3);
-            % 打印接收到的命令（仅用于调试）
-            disp(['Received command: ', command]);
-            
-            % 如果接收到 'exit' 命令，则退出循环
-            if strcmp(command, 'exit')
-                break;
-            else
-                % 计算命令结果（注意：此处使用 eval 是不安全的，仅用于示例）
-                %result = eval(command);
-                AutoOpt('Mode','solve','Problem','pbo','InstanceSolve',[problem_dim],'AlgName',"Discrete Simulated Annealing",'inital_t',T,'ProbN',50,'ProbFE',50000,'AlgRuns',1,'problem_id',problem_id);
-                load(strcat("Discrete Simulated Annealing",int2str(problem_id)));
-                res =[];
-                [x, y] = size(bestSolutions);
-                for ii =1: y
-                    res(ii)=cat(1,bestSolutions(1,ii).fit);
-                end
-                % 将结果转换为字符串并发送回客户端
-                fwrite(t, num2str(median(res)));
-                disp(median(res));
-            end
-        else
-            % 暂停片刻以避免忙等待
-            pause(0.1);
-        end
-    end
-catch ME
-    disp('An error occurred:');
-    disp(ME.message);
-end
-
-% 确保在任何情况下都能关闭连接并清理对象
-if exist('t', 'var') && isvalid(t)
-    fclose(t);
-    delete(t);
-    clear t;
-end
-disp('MATLAB server has stopped.');
diff --git a/matlab/test_function.m b/matlab/test_function.m
deleted file mode 100644
index f3ae998..0000000
--- a/matlab/test_function.m
+++ /dev/null
@@ -1,14 +0,0 @@
-
-addpath(genpath('Utilities'));
-addpath(genpath('Problems'));
-addpath(genpath('Main'));
-addpath(genpath('Components'));
-
-% 没开一次matlab 只执行一次
-%pyversion('C:\Users\21902\.conda\envs\trrl\python.exe')
-
-%py.IOH_Test.cosntrains()
-%res = py.IOH_Test.test()
-seq =[ 0, 31, 24,  7, 23, 31, 24,  9, 23, 31, 24,  6, 23, 31, 24,  8, 29, 14, 29];
-problem_id = 8;
-performance = get_perf(seq,problem_id,[4],1)
\ No newline at end of file
diff --git a/matlab_setting.py b/matlab_setting.py
deleted file mode 100644
index 29cf870..0000000
--- a/matlab_setting.py
+++ /dev/null
@@ -1,113 +0,0 @@
-import warnings
-
-problem_type = 'discrete'  # continuous/discrete
-
-
-def is_ls_para(opera_index, para_index, para_num=1):
-    # para_num ,first para or second para
-    if opera_index == 6 or opera_index == 7 or opera_index == 8:  # ls:0-0.3 paraSpace:0-1
-        return ~(para_index > 30)
-
-    if opera_index == 13 or opera_index == 17:  # 13: ls:0-0.15 paraSpace:0-0.5  #17: ls:0-0.9 paraSpace:0-0.3
-        return ~(para_index > 30)
-
-    if opera_index == 16:  # ls:0-0.09;28-40 paraSpace:0-0.3;20-40
-        if para_num == 1:
-            return ~(para_index > 30)
-        elif para_num == 2:
-            return (para_index > 30)
-
-    if opera_index == 17:  # ls:0-0.9 paraSpace:0-0.3
-        return ~(para_index > 30)
-
-
-def dis_is_ls_opera(opera_index, para_index, para_num=1):
-    if opera_index == 4 or opera_index == 5 or opera_index == 10:
-        return False
-    elif opera_index == 8:
-        return True
-    else:
-        return ~(para_index > 20)
-
-
-operator_para = {
-    "choose_traverse": 0,  # 0
-    "choose_tournament": 0,  # 1
-    "choose_roulette_wheel": 0,  # 2
-    "choose_nich": 0,  # 3
-
-    'cross_point_one': 0,  # 4
-    'cross_point_two': 0,  # 5
-    'cross_point_n': 1,  # 6
-    'cross_point_uniform': 1,  # 7
-    'search_reset_one': 0,  # 8
-    'search_reset_n': 1,  # 9
-    'search_reset_rand': 1,  # 10
-    'reinit_discrete': 0,  # 11
-
-    'update_greedy': 0,  # 12
-    'update_round_robin': 0,  # 13
-    'update_pairwise': 0,  # 14
-    'update_always': 0,  # 15
-    'update_simulated_annealing': 1,  # 16
-
-    "begin": 0,  # 17
-    "end": 0,  # 18
-    "0.1": 0,  # 19
-    "0.2": 0,  # 20
-    "0.3": 0,  # 21
-    "0.4": 0,  # 22
-    "0.5": 0,  # 23
-    "0.6": 0,  # 24
-    "0.7": 0,  # 25
-    "0.8": 0,  # 26
-    "0.9": 0,  # 27
-    "1.0": 0,  # 28
-
-    "forward": 0,  # 29, count condition as 1 execution time
-    "iterate": 1,  # 30, with 5 candidate count conditions
-    "fork": 1,  # 31, count condition as 1 execution time
-}  # 32 in total
-
-voc_size = operator_para.__len__()
-voc_list = list(operator_para.keys())
-need_para_list = list(operator_para.values())
-choose_begin = 0
-choose_num = 4
-choose_end = 4
-update_begin = 12
-update_num = 5
-update_end = 16
-para_begin = 19
-para_num = 10
-para_end = 28
-search_begin = 4
-search_num = 8
-search_end = 11
-cross_begin = 4
-cross_num = 4
-cross_end = 7
-mu_begin = 8
-mu_num = 3
-mu_end = 10
-operators_begin = 0
-operators_num = 17
-operators_end = 16
-ptrs_begin = 29
-ptrs_num = 3
-ptrs_end = 31
-begin_index = 17
-end_index = 18
-total_index = 32
-
-forward_index = 29
-iterate_index = 30
-fork_index = 31
-
-# para decide opera is LS or GS
-LS_range = 0.3
-# operators only can be Global Search ,python index,from 0 begin
-only_gs_opera_index = [4, 5, 11]
-gs_or_ls_opera_index = [6, 7, 9, 10]
-max_operators = 6
-gs_para_begin = 22
\ No newline at end of file
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deleted file mode 100644
index 605866d..0000000
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deleted file mode 100644
index c5bc27f..0000000
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diff --git a/models/blocks/__pycache__/decoder_layer.cpython-38.pyc b/models/blocks/__pycache__/decoder_layer.cpython-38.pyc
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index 1ff4bc9..0000000
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diff --git a/models/blocks/__pycache__/encoder_layer.cpython-38.pyc b/models/blocks/__pycache__/encoder_layer.cpython-38.pyc
deleted file mode 100644
index 102cc73..0000000
Binary files a/models/blocks/__pycache__/encoder_layer.cpython-38.pyc and /dev/null differ
diff --git a/models/blocks/decoder_layer.py b/models/blocks/decoder_layer.py
index dbc6d5b..1515495 100644
--- a/models/blocks/decoder_layer.py
+++ b/models/blocks/decoder_layer.py
@@ -1,4 +1,3 @@
-import torch
 from torch import nn
 
 from models.layers.layer_norm import LayerNorm
@@ -6,9 +5,7 @@
 from models.layers.position_wise_feed_forward import PositionwiseFeedForward
 
 
-
 class DecoderLayer(nn.Module):
-
     def __init__(self, d_model, ffn_hidden, n_head, drop_prob):
         super(DecoderLayer, self).__init__()
         self.self_attention = MultiHeadAttention(d_model=d_model, n_head=n_head)
@@ -19,7 +16,9 @@ def __init__(self, d_model, ffn_hidden, n_head, drop_prob):
         self.norm2 = LayerNorm(d_model=d_model)
         self.dropout2 = nn.Dropout(p=drop_prob)
 
-        self.ffn = PositionwiseFeedForward(d_model=d_model, hidden=ffn_hidden, drop_prob=drop_prob)
+        self.ffn = PositionwiseFeedForward(
+            d_model=d_model, hidden=ffn_hidden, drop_prob=drop_prob
+        )
         self.norm3 = LayerNorm(d_model=d_model)
         self.dropout3 = nn.Dropout(p=drop_prob)
 
@@ -31,13 +30,13 @@ def forward(self, dec, trg_mask):
         x = self.self_attention(q=dec, k=dec, v=dec, mask=trg_mask)
 
         # 2. add and norm
-        #x = self.dropout1(x)
+        # x = self.dropout1(x)
         x = self.norm1(x + _x)
 
         # 5. positionwise feed forward network
         _x = x
         x = self.ffn(x)
-        
+
         # 6. add and norm
         x = self.norm3(x + _x)
 
diff --git a/models/blocks/encoder_layer.py b/models/blocks/encoder_layer.py
deleted file mode 100644
index 716ad5e..0000000
--- a/models/blocks/encoder_layer.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import torch
-from torch import nn
-
-from models.layers.layer_norm import LayerNorm
-from models.layers.multi_head_attention import MultiHeadAttention
-from models.layers.position_wise_feed_forward import PositionwiseFeedForward
-
-
-class EncoderLayer(nn.Module):
-
-    def __init__(self, d_model, ffn_hidden, n_head, drop_prob):
-        super(EncoderLayer, self).__init__()
-        self.attention = MultiHeadAttention(d_model=d_model, n_head=n_head)
-        self.norm1 = LayerNorm(d_model=d_model)
-        self.dropout1 = nn.Dropout(p=drop_prob)
-
-        self.ffn = PositionwiseFeedForward(d_model=d_model, hidden=ffn_hidden, drop_prob=drop_prob)
-        self.norm2 = LayerNorm(d_model=d_model)
-    #self.dropout2 = nn.Dropout(p=drop_prob)
-
-    def forward(self, x, src_mask):
-        # 1. compute self attention
-        _x = x
-        x = self.attention(q=x, k=x, v=x, mask=src_mask)
-        
-        # 2. add and norm
-        #x = self.dropout1(x)
-        x = self.norm1(x + _x)
-        
-        # 3. positionwise feed forward network
-        _x = x
-        x = self.ffn(x)
-      
-        # 4. add and norm
-        #x = self.dropout2(x)
-        x = self.norm2(x + _x)
-
-        return x
-
diff --git a/models/embedding/__init__.py b/models/embedding/__init__.py
index d5b062b..89f1cc0 100644
--- a/models/embedding/__init__.py
+++ b/models/embedding/__init__.py
@@ -2,4 +2,4 @@
 @author : Hyunwoong
 @when : 2019-10-22
 @homepage : https://github.com/gusdnd852
-"""
\ No newline at end of file
+"""
diff --git a/models/embedding/__pycache__/__init__.cpython-38.pyc b/models/embedding/__pycache__/__init__.cpython-38.pyc
deleted file mode 100644
index 4b4fc38..0000000
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diff --git a/models/embedding/__pycache__/positional_encoding.cpython-38.pyc b/models/embedding/__pycache__/positional_encoding.cpython-38.pyc
deleted file mode 100644
index 2f94a3c..0000000
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diff --git a/models/embedding/__pycache__/token_embeddings.cpython-38.pyc b/models/embedding/__pycache__/token_embeddings.cpython-38.pyc
deleted file mode 100644
index cf08364..0000000
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diff --git a/models/embedding/__pycache__/transformer_embedding.cpython-38.pyc b/models/embedding/__pycache__/transformer_embedding.cpython-38.pyc
deleted file mode 100644
index 0d08c0a..0000000
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diff --git a/models/embedding/positional_encoding.py b/models/embedding/positional_encoding.py
index 2b64ea4..9006c3e 100644
--- a/models/embedding/positional_encoding.py
+++ b/models/embedding/positional_encoding.py
@@ -18,8 +18,7 @@ def __init__(self, d_model, max_len, device):
         super(PositionalEncoding, self).__init__()
 
         # same size with input matrix (for adding with input matrix)
-        self.encoding = torch.zeros(max_len, d_model, device=device)
-        self.encoding.requires_grad = False  # we don't need to compute gradient
+        encoding = torch.zeros(max_len, d_model, device=device)
 
         pos = torch.arange(0, max_len, device=device)
         pos = pos.float().unsqueeze(dim=1)
@@ -29,8 +28,9 @@ def __init__(self, d_model, max_len, device):
         # 'i' means index of d_model (e.g. embedding size = 50, 'i' = [0,50])
         # "step=2" means 'i' multiplied with two (same with 2 * i)
 
-        self.encoding[:, 0::2] = torch.sin(pos / (10000 ** (_2i / d_model)))
-        self.encoding[:, 1::2] = torch.cos(pos / (10000 ** (_2i / d_model)))
+        encoding[:, 0::2] = torch.sin(pos / (10000 ** (_2i / d_model)))
+        encoding[:, 1::2] = torch.cos(pos / (10000 ** (_2i / d_model)))
+        self.register_buffer("encoding", encoding, persistent=False)
         # compute positional encoding to consider positional information of words
 
     def forward(self, x):
diff --git a/models/embedding/token_embeddings.py b/models/embedding/token_embeddings.py
index 62f3285..40a5ff3 100644
--- a/models/embedding/token_embeddings.py
+++ b/models/embedding/token_embeddings.py
@@ -3,6 +3,7 @@
 @when : 2019-10-24
 @homepage : https://github.com/gusdnd852
 """
+
 from torch import nn
 
 
@@ -19,4 +20,6 @@ class for token embedding that included positional information
         :param vocab_size: size of vocabulary
         :param d_model: dimensions of model
         """
-        super(TokenEmbedding, self).__init__(vocab_size, d_model, padding_idx=1)
+        # ALDes has no padding token. Index 1 is choose_tournament and must
+        # receive gradients like every other vocabulary entry.
+        super(TokenEmbedding, self).__init__(vocab_size, d_model)
diff --git a/models/embedding/transformer_embedding.py b/models/embedding/transformer_embedding.py
index 8efc847..4b9e1e8 100644
--- a/models/embedding/transformer_embedding.py
+++ b/models/embedding/transformer_embedding.py
@@ -26,4 +26,4 @@ def forward(self, x):
         tok_emb = self.tok_emb(x)
         pos_emb = self.pos_emb(x)
 
-        return tok_emb + pos_emb
+        return self.drop_out(tok_emb + pos_emb)
diff --git a/models/layers/__pycache__/__init__.cpython-38.pyc b/models/layers/__pycache__/__init__.cpython-38.pyc
deleted file mode 100644
index d68c8a1..0000000
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diff --git a/models/layers/__pycache__/layer_norm.cpython-38.pyc b/models/layers/__pycache__/layer_norm.cpython-38.pyc
deleted file mode 100644
index 8311673..0000000
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diff --git a/models/layers/__pycache__/multi_head_attention.cpython-38.pyc b/models/layers/__pycache__/multi_head_attention.cpython-38.pyc
deleted file mode 100644
index 2247e4d..0000000
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diff --git a/models/layers/__pycache__/position_wise_feed_forward.cpython-38.pyc b/models/layers/__pycache__/position_wise_feed_forward.cpython-38.pyc
deleted file mode 100644
index 7b83f65..0000000
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diff --git a/models/layers/__pycache__/scale_dot_product_attention.cpython-38.pyc b/models/layers/__pycache__/scale_dot_product_attention.cpython-38.pyc
deleted file mode 100644
index 89923f8..0000000
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diff --git a/models/layers/multi_head_attention.py b/models/layers/multi_head_attention.py
index a53af8f..57d0eaa 100644
--- a/models/layers/multi_head_attention.py
+++ b/models/layers/multi_head_attention.py
@@ -4,7 +4,6 @@
 
 
 class MultiHeadAttention(nn.Module):
-
     def __init__(self, d_model, n_head):
         super(MultiHeadAttention, self).__init__()
         self.n_head = n_head
diff --git a/models/layers/position_wise_feed_forward.py b/models/layers/position_wise_feed_forward.py
index 139f009..c506916 100644
--- a/models/layers/position_wise_feed_forward.py
+++ b/models/layers/position_wise_feed_forward.py
@@ -2,7 +2,6 @@
 
 
 class PositionwiseFeedForward(nn.Module):
-
     def __init__(self, d_model, hidden, drop_prob=0.1):
         super(PositionwiseFeedForward, self).__init__()
         self.linear1 = nn.Linear(d_model, hidden)
@@ -13,6 +12,6 @@ def __init__(self, d_model, hidden, drop_prob=0.1):
     def forward(self, x):
         x = self.linear1(x)
         x = self.relu(x)
-        #x = self.dropout(x)
+        # x = self.dropout(x)
         x = self.linear2(x)
         return x
diff --git a/models/layers/scale_dot_product_attention.py b/models/layers/scale_dot_product_attention.py
index c2065a7..827dbf1 100644
--- a/models/layers/scale_dot_product_attention.py
+++ b/models/layers/scale_dot_product_attention.py
@@ -3,46 +3,30 @@
 @when : 2019-10-22
 @homepage : https://github.com/gusdnd852
 """
+
 import math
 
 from torch import nn
 
 
 class ScaleDotProductAttention(nn.Module):
-    """
-    compute scale dot product attention
-
-    Query : given sentence that we focused on (decoder)
-    Key : every sentence to check relationship with Qeury(encoder)
-    Value : every sentence same with Key (encoder)
-    """
+    """Compute scaled dot-product attention."""
 
     def __init__(self):
         super(ScaleDotProductAttention, self).__init__()
         self.softmax = nn.Softmax(dim=-1)
 
-    def forward(self, q, k, v, mask=None, e=1e-12):
-        # input is 4 dimension tensor
-        # [batch_size, head, length, d_tensor]
-        batch_size, head, length, d_tensor = k.size()
-
-        # 1. dot product Query with Key^T to compute similarity
-        k_t = k.transpose(2, 3)  # transpose
-        score = (q @ k_t) / math.sqrt(d_tensor)  # scaled dot product
+    def forward(self, q, k, v, mask=None):
+        # Inputs have shape [batch, head, sequence length, head dimension].
+        d_tensor = k.size(-1)
 
-        # bug here? our mask make rows all zero but after softmax get a average value
+        k_t = k.transpose(2, 3)
+        score = (q @ k_t) / math.sqrt(d_tensor)
 
-        # 2. apply masking (opt)
         if mask is not None:
             score = score.masked_fill(mask == 0, -10000)
 
-        # 3. pass them softmax to make [0, 1] range
         score = self.softmax(score)
-
-        # softmax make 0-row get a average value,here make these 0-value-rows change to zero again, not sure if there right
-
-        #score = score.masked_fill(mask == 0,0)
-        # 4. multiply with Value
         v = score @ v
 
         return v, score
diff --git a/models/model/__pycache__/__init__.cpython-38.pyc b/models/model/__pycache__/__init__.cpython-38.pyc
deleted file mode 100644
index 5eb2534..0000000
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diff --git a/models/model/__pycache__/decoder.cpython-38.pyc b/models/model/__pycache__/decoder.cpython-38.pyc
deleted file mode 100644
index 05f0650..0000000
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diff --git a/models/model/__pycache__/encoder.cpython-38.pyc b/models/model/__pycache__/encoder.cpython-38.pyc
deleted file mode 100644
index e0eba69..0000000
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diff --git a/models/model/__pycache__/transformer.cpython-38.pyc b/models/model/__pycache__/transformer.cpython-38.pyc
deleted file mode 100644
index 261e7e6..0000000
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diff --git a/models/model/decoder.py b/models/model/decoder.py
index a009f42..e1d091f 100644
--- a/models/model/decoder.py
+++ b/models/model/decoder.py
@@ -1,537 +1,134 @@
+"""Autoregressive decoder used by the standalone ALDes training script."""
+
+from __future__ import annotations
+
+import math
+
 import numpy as np
 import torch
 from torch import nn
-import torch.nn.functional as F
-from torch.distributions import Categorical
+
+from aldes_setting import end_index
+from autooptlib.aldes.vocabulary import allowed_next_tokens
 from models.blocks.decoder_layer import DecoderLayer
 from models.embedding.transformer_embedding import TransformerEmbedding
-from torch.distributions.normal import Normal
-from numpy import*
-from torch.nn import Parameter
-from matlab_setting import *
-use_Point = False
-
-
-class GatingMechanism(torch.nn.Module):
-    def __init__(self, d_input, bg=0.1):
-        super(GatingMechanism, self).__init__()
-        self.Wr = torch.nn.Linear(d_input, d_input)
-        self.Ur = torch.nn.Linear(d_input, d_input)
-        self.Wz = torch.nn.Linear(d_input, d_input)
-        self.Uz = torch.nn.Linear(d_input, d_input)
-        self.Wg = torch.nn.Linear(d_input, d_input)
-        self.Ug = torch.nn.Linear(d_input, d_input)
-        self.bg = bg
 
-        self.sigmoid = torch.nn.Sigmoid()
-        self.tanh = torch.nn.Tanh()
 
-    def forward(self, x, y):
-        r = self.sigmoid(self.Wr(y) + self.Ur(x))
-        z = self.sigmoid(self.Wz(y) + self.Uz(x) - self.bg)
-        h = self.tanh(self.Wg(y) + self.Ug(torch.mul(r, x)))
-        g = torch.mul(1 - z, x) + torch.mul(z, h)
-        return g
 class Decoder(nn.Module):
-    def __init__(self, dec_voc_size, max_len, d_model, ffn_hidden, n_head, n_layers, drop_prob, device):
+    def __init__(
+        self,
+        dec_voc_size,
+        max_len,
+        d_model,
+        ffn_hidden,
+        n_head,
+        n_layers,
+        drop_prob,
+        device,
+        condition_on_features=False,
+    ):
         super().__init__()
-        self.emb = TransformerEmbedding(d_model=d_model,
-                                        drop_prob=drop_prob,
-                                        max_len=max_len,
-                                        vocab_size=dec_voc_size,
-                                        device=device)
-
-        self.layers = nn.ModuleList([DecoderLayer(d_model=d_model,
-                                                  ffn_hidden=ffn_hidden,
-                                                  n_head=n_head,
-                                                  drop_prob=drop_prob)
-                                     for _ in range(n_layers)])
-
+        self.emb = TransformerEmbedding(
+            d_model=d_model,
+            drop_prob=drop_prob,
+            max_len=max_len,
+            vocab_size=dec_voc_size,
+            device=device,
+        )
+        self.layers = nn.ModuleList(
+            [
+                DecoderLayer(
+                    d_model=d_model,
+                    ffn_hidden=ffn_hidden,
+                    n_head=n_head,
+                    drop_prob=drop_prob,
+                )
+                for _ in range(n_layers)
+            ]
+        )
         self.linear = nn.Linear(d_model, dec_voc_size)
-        self.sigmoid = nn.Sigmoid()
-        self.softmax =nn.Softmax(dim=2)
         self.device = device
+        self.max_len = max_len
+        self.condition_on_features = condition_on_features
+        self.feature_projection = (
+            nn.Linear(d_model, d_model) if condition_on_features else None
+        )
         self.temp = 1.0
-        self.input_linear = nn.Linear(d_model, d_model)
-        self.action_linear = nn.Conv1d(d_model, d_model, 1, 1)
-        self.V = Parameter(torch.FloatTensor(d_model), requires_grad=True)
-        self.tanh = nn.Tanh()
-        self.attn = nn.Linear(d_model, d_model)
-
-        # Initialize vector V
-        nn.init.uniform(self.V, -1, 1)
-
-        self.gate = GatingMechanism(d_model)
-
-    def get_action(self, model_output, action = None):
-
-        model_output = torch.squeeze(model_output)
-        sdv = model_output[1::2]
-        mean = model_output[0::2]
-        # here use exp make sure its positive
-        sdv = torch.exp(sdv)
-
-        probs = Normal(mean, sdv)
-        if action is None:
-            action = probs.sample()
-
-        return action, probs.log_prob(action), probs.entropy()
-
-    def pointer(self, out, action_emb):
-        out = self.input_linear(out).expand(-1,action_emb.size(1),-1)
-        out = out.permute(0, 2, 1)
-        action_emb = action_emb.permute(0,2,1)
-        action_emb = self.action_linear(action_emb)
-        # (batch, 1, hidden_dim)
-        V = self.V.unsqueeze(0).expand(action_emb.size(0), -1).unsqueeze(1)
-
-        # (batch, seq_len)
-        att = torch.bmm(V, self.tanh(out + action_emb)).squeeze(1)
-        return att
-    def forward(self, trg, enc_src, action_emb,trg_mask, src_mask,action = None,inference = False):
-
-        action_p=[]
-        action_log_p=[]
-        operator_num = torch.zeros(trg.shape,dtype=torch.int64).to(self.device)
-        need_para_num = torch.zeros(trg.shape,dtype=torch.int64).to(self.device)
-        action_index = torch.zeros(trg.shape).to(self.device) # 'begin'
-        last_opera_index =torch.zeros(trg.shape,dtype=torch.int64).to(self.device)
-        last_normal_opera_index = torch.zeros(trg.shape,dtype=torch.int64).to(self.device)
-        have_gs = torch.zeros(trg.shape, dtype=torch.int64).to(self.device)
-        action_index[:,:]=begin_index
-        last_opera_index[:,:]=begin_index
-        finished = torch.zeros(trg.shape).to(self.device)
-        all_finished = torch.ones(trg.shape).to(self.device)
 
+    def forward(
+        self,
+        trg,
+        enc_src,
+        action_emb,
+        trg_mask,
+        src_mask,
+        action=None,
+        inference=False,
+    ):
+        del action_emb, trg_mask, src_mask
+        action_p = []
+        action_log_p = []
         ppo_index = 1
 
-        #for i in range(0,16):
-        while(True):
-            #code here : drop finished row
-            #unfinished_index,last_opera_index,last_normal_opera_index, operator_num,need_para_num, unfinished_trg,unfinished_have_gs,unfinished_action_emb,unfinished_enc_src\
-            #    = self.choose_unfinished(finished,have_gs,last_opera_index,last_normal_opera_index,operator_num,need_para_num,trg,action_emb,enc_src)
-
-            input = self.emb(trg)
-
-            #input = torch.cat((unfinished_enc_src, trg_emb), dim=1)
+        while True:
+            if trg.shape[1] >= self.max_len:
+                raise RuntimeError("ALDes generation reached max_len before end.")
+
+            decoder_input = self.emb(trg)
+            if self.condition_on_features:
+                if enc_src is None:
+                    raise ValueError("Continual ALDes mode requires problem features.")
+                feature = enc_src
+                if feature.ndim == 3:
+                    feature = feature.mean(dim=1)
+                if feature.ndim != 2 or feature.shape[-1] != decoder_input.shape[-1]:
+                    raise ValueError(
+                        "Problem features must have shape (batch, 32) or "
+                        "(batch, samples, 32)."
+                    )
+                decoder_input = decoder_input + self.feature_projection(
+                    feature
+                ).unsqueeze(1)
 
             for layer in self.layers:
-                 input = layer(input,trg_mask = None)
-
-            # pass to LM head
-            output = self.linear(input)
-            output = output[:,-1:,:]
-            #mask = self.get_mask_mat(unfinished_trg, action_index, last_opera_index,last_normal_opera_index, operator_num, need_para_num, unfinished_have_gs, finished).to(self.device)
-            mask = self.get_mask(trg)
-            mask = torch.unsqueeze(mask,1)
-            output = output.masked_fill(mask == 0, -math.inf)
-            # See https://discuss.pytorch.org/t/bad-behavior-of-multinomial-function/10232
-            _log_p = torch.log_softmax(output / self.temp, dim=-1)
-            distribution = _log_p.exp()
-            if torch.isnan(distribution).any() or torch.isinf(distribution).any():
-                print("nan or inf")
+                decoder_input = layer(decoder_input, trg_mask=None)
+            output = self.linear(decoder_input[:, -1:, :])
+            mask = self.get_mask(trg).unsqueeze(1)
+            output = output.masked_fill(~mask, -math.inf)
+            log_probabilities = torch.log_softmax(output / self.temp, dim=-1)
+            probabilities = log_probabilities.exp()
 
             if action is None:
-                action_index = distribution.squeeze(1).multinomial(1)
-                if inference is True:action_index = torch.argmax(distribution.squeeze(1), dim=1).unsqueeze(1)
-                while not mask.squeeze(1).gather(1, action_index).data.all():
-                    print('Sampled bad values, resampling!')
-                    action_index = output.multinomial(1).squeeze(1)
-                    if inference is True: action_index = torch.argmax(distribution.squeeze(1), dim=1).unsqueeze(1)
+                if inference:
+                    action_index = probabilities.argmax(dim=-1)
+                else:
+                    action_index = probabilities.squeeze(1).multinomial(1)
             else:
-                # action should be full rows
-                unfinished_index = torch.where(finished == 0)[0]
-                temp_action = torch.index_select(action, 0, unfinished_index)
-                action_index = temp_action[:,ppo_index] # "begin" is first
+                if ppo_index >= action.shape[1]:
+                    raise ValueError("Replay action ends before the end token.")
+                action_index = action[:, ppo_index].unsqueeze(1)
                 ppo_index += 1
-                action_index = action_index.unsqueeze(1)
-
-            # Get log_p corresponding to selected actions
-            p = distribution[0, 0, action_index]
-            log_p = _log_p.gather(2, action_index.unsqueeze(-1)).squeeze(-1)
 
+            probability = probabilities.gather(2, action_index.unsqueeze(-1)).squeeze(
+                -1
+            )
+            log_probability = log_probabilities.gather(
+                2, action_index.unsqueeze(-1)
+            ).squeeze(-1)
             trg = torch.cat([trg, action_index], dim=1)
-            action_p.append(p)
-            action_log_p.append(log_p)
+            action_p.append(probability)
+            action_log_p.append(log_probability)
             if torch.all(action_index == end_index):
                 break
 
-        trg = self.check_pointer(trg)
-        return trg, torch.stack(action_p,1),torch.stack(action_log_p,1)
-
-    # if pointer point more than the alg operator length, fix it
-    def check_pointer(self,cur_alg):
-        dim0, dim1 = cur_alg.shape
-        for i in range(dim0):
-            cur_opera_num = 0
-            cur_operas = []
-            for action in cur_alg[i]:
-                if operators_begin <= action <= operators_end:
-                    cur_opera_num += 1
-                    cur_operas.append(action)
+        return trg, torch.stack(action_p, 1), torch.stack(action_log_p, 1)
 
-            positions = torch.nonzero(torch.eq(cur_alg[i], fork_index))
-            for position in positions:
-                if cur_alg[i][position + 1] > para_begin + cur_opera_num - 1:
-                    cur_alg[i][position + 1] = para_begin + cur_opera_num - 1
+    @staticmethod
+    def check_pointer(cur_alg):
+        """Compatibility no-op; AutoOptLib safely interprets fork targets."""
 
         return cur_alg
 
     def get_mask(self, cur_alg):
-
-        dim0,dim1 = cur_alg.shape
-        # 0 means action is masked
-        mask = torch.zeros([dim0, voc_size], dtype=torch.double).to(self.device)
-        for i in range(dim0):
-            last_action = cur_alg[i][-1]
-
-            # first action must be 'choose'
-            if last_action == begin_index:
-                mask[i][choose_begin:choose_end] = 1
-                continue
-
-            # last action is "end"
-            if last_action == end_index:
-                mask[i][end_index] = 1
-                continue
-
-            tmp = -1
-            # last not para action is operator or ptr
-            last_opera_or_ptrs = True
-            last_opera = None
-            last_ptrs = None
-            while True:
-                if operators_begin <= cur_alg[i][tmp] <= operators_end and last_opera is None:
-                    last_opera = cur_alg[i][tmp]
-                if ptrs_begin <= cur_alg[i][tmp] <= ptrs_end and last_ptrs is None:
-                    last_ptrs = cur_alg[i][tmp]
-                if (last_ptrs is not None and last_opera is not None) or cur_alg[i][tmp] == begin_index:
-                    break
-                tmp -= 1
-            tmp = -1
-            while True:
-                if operators_begin <= cur_alg[i][tmp] <= operators_end:
-                    last_opera_or_ptrs = True
-                    break
-                if ptrs_begin <= cur_alg[i][tmp] <= ptrs_end:
-                    last_opera_or_ptrs = False
-                    break
-                tmp -= 1
-
-
-            # calculate total operators of cur alg
-            tmp = -1
-            cur_opera_num = 0
-            cur_operas = []
-            for action in cur_alg[i]:
-                if operators_begin <= action <= operators_end:
-                    cur_opera_num += 1
-                    cur_operas.append(action)
-
-            operator_need_para = False
-            need_operator_or_end = False
-            # last action is operator
-            if operators_begin <= last_action <= operators_end:
-                if need_para_list[last_action] != 0:
-                    mask[i][para_begin:para_end + 1] = 1
-                    operator_need_para = True
-                else:
-                    # operator have no para, need pointer
-                    if choose_begin <= last_action <= choose_end:
-                        mask[i][forward_index] = 1
-                        mask[i][fork_index] = 1
-                    elif search_begin <= last_action <= search_end:
-                        mask[i][ptrs_begin:ptrs_end + 1] = 1
-                    elif update_begin <= last_action <= update_end:
-                        mask[i][forward_index] = 1
-                        #mask[i][iterate_index] = 1
-
-            # last action is para
-            elif para_begin <= last_action <= para_end:
-                para_enough = False
-                # para of operator
-                if last_opera_or_ptrs:
-                    if para_begin <= cur_alg[i][-2] <= para_end:
-                        para_enough = True
-                    else:
-                        if need_para_list[last_opera] == 1:
-                            para_enough = True
-                    if para_enough:
-                        # opear,para,need ptr
-                        if choose_begin <= last_opera <= choose_end:
-                            mask[i][forward_index] = 1
-                            mask[i][fork_index] = 1
-                        elif search_begin <= last_opera <= search_end:
-                            mask[i][ptrs_begin:ptrs_end + 1] = 1
-                        elif update_begin <= last_opera <= update_end:
-                            mask[i][forward_index] = 1
-                            mask[i][iterate_index] = 1
-                    else:
-                        mask[i][para_begin:para_end + 1] = 1
-                        operator_need_para = True
-                # para of ptrs
-                else:
-                    # ptrs most have one para, next is operator/end
-                    need_operator_or_end = True
-
-            # last action is pointer
-            elif ptrs_begin <= last_action <= ptrs_end:
-                if last_action == forward_index:
-                    # forward have no condition
-                    need_operator_or_end = True
-                elif last_action == iterate_index:
-                    # iterate only follow para in 1-5
-                    mask[i][para_begin:para_begin + 5] = 1
-                elif last_action == fork_index:
-                    mask[i][para_begin + cur_opera_num + 1 : para_begin + max_operators] = 1
-                    if para_begin + cur_opera_num + 1 >= max_operators :
-                        mask[i][:] = 0
-                        mask[i][para_begin + max_operators - 1] = 1
-
-            if need_operator_or_end:
-                if cur_opera_num == 1:
-                    mask[i][search_begin:search_end + 1] = 1
-                    # first search not be mu
-                    mask[i][mu_begin:mu_end + 1] = 0
-                elif 1 < cur_opera_num < max_operators - 2:
-                    mask[i][search_begin:search_end + 1] = 1
-                    mask[i][update_begin:update_end + 1] = 1
-                elif cur_opera_num >= max_operators - 2:
-                    mask[i] = 0
-                    mask[i][update_begin:update_end + 1] = 1
-
-                if update_begin <= last_opera <= update_end:
-                    mask[i][:] = 0
-                    mask[i][end_index] = 1
-
-                # if last operator is crossover,next must be mutation
-                if cross_begin <= last_opera <= cross_end:
-                    mask[i][:] = 0
-                    mask[i][mu_begin:mu_end + 1] = 1
-                # mask already selected operators
-                for select_opeartor in cur_operas:
-                    mask[i][select_opeartor] = 0
-
-            # here need mask global para or operator
-            have_global_opera = False
-            for j in range(len(cur_alg[i])):
-                action = cur_alg[i][j]
-                if operators_begin <= action <= operators_end:
-                    if action in only_gs_opera_index:
-                        have_global_opera = True
-                    # no para search only be local
-                    elif need_para_list[action] >= 1:
-                        # all search most have one para, para>0.3 is GS
-                        if j != len(cur_alg[i]) -1 and cur_alg[i][j+1] >= gs_para_begin:
-                            have_global_opera = True
-            if have_global_opera:
-                mask[i][only_gs_opera_index] = 0
-                if operator_need_para:
-                    mask[i][para_begin:gs_para_begin] = 0
-
-            if mask[i].sum() == 0:
-                print("all mask ! ERROR")
-
-        return mask
-
-
-
-
-
-
-
-
-
-    def get_mask_mat(self, trg, action_index,last_opera_index,last_normal_opera_index, operator_num, need_para_num,have_gs,finished):
-
-        dim0,dim1 = trg.shape
-
-        last_is_prts = (last_opera_index >= ptrs_begin) * (last_opera_index <= ptrs_begin + ptrs_num - 1)
-        last_normal_is_cross = (last_normal_opera_index<=cross_begin+cross_num-1) * (last_normal_opera_index>=cross_begin)
-
-        # 0 is masked
-        mask = torch.ones([dim0,voc_size],dtype=torch.double).to(self.device)
-
-        # mask parameter
-        mask[:,para_begin:para_begin+para_num] = torch.where(need_para_num!=0, mask[:,para_begin:para_begin+para_num],0.)
-
-        # mask choose operator
-        choose_cond = (operator_num == 0) * (need_para_num == 0)
-        mask[:, choose_begin:choose_begin + choose_num] = torch.where(choose_cond, mask[:, choose_begin:choose_begin + choose_num], 0.)
-        # mask update operator
-        selet_cond = (operator_num == 1) * (need_para_num == 0)
-        mask[:, update_begin:update_begin+update_num] = torch.where(selet_cond, mask[:, update_begin:update_begin+update_num], 0.)
-
-        # mask search operator
-        search_cond = (operator_num >= 2) * (need_para_num == 0)
-        mask[:, search_begin:search_begin + search_num] = torch.where(search_cond, mask[:, search_begin:search_begin + search_num], 0.)
-        # crossover must followed by mutation,if last operator is crossover, mask operators unless mutation
-        cross_cond = last_normal_is_cross* (need_para_num == 0)
-        # first mask all operator and unmask mutation
-        mask[:, search_begin:search_begin + search_num] = torch.where(cross_cond, 0. ,mask[:, search_begin:search_begin + search_num])
-        mask[:, mu_begin:mu_begin+mu_num] = torch.where(cross_cond,1.,mask[:, mu_begin:mu_begin+mu_num])
-
-        #first search not be mutation
-        first_search = (operator_num == 2)
-        mask[:, mu_begin:mu_begin + mu_num] = torch.where(first_search, 0., mask[:, mu_begin:mu_begin + mu_num])
-
-        # mask seleted operator, not contain ptrs
-        for i in range(dim0):
-            for j in range(dim1):
-                opera = trg[i][j].item()
-                if opera<=operators_num: mask[i,opera] = 0.
-
-        # alg have search, choose, update, can end, add "End"
-        mask[:,end_index]=0
-        temp = torch.ones([dim0,total_index],dtype=torch.double).to(self.device)
-        can_end = (operator_num >=4) * (need_para_num ==0) * (~last_normal_is_cross) *(last_is_prts)
-        mask[:,end_index:end_index+1] = torch.where(can_end ,temp[:,end_index:end_index+1],mask[:,end_index:end_index+1])
-
-        # last operator cant be cross
-        last_opera = (operator_num >=8)*(need_para_num == 0)
-        mask[:, cross_begin:cross_begin +cross_num] = torch.where(last_opera,0.,mask[:, cross_begin:cross_begin +cross_num])
-
-        # force select ptrs after search opera and para
-        need_ptrs = (need_para_num == 0) * (last_opera_index >= search_begin) * (last_opera_index <= search_begin + search_num - 1)
-        need_ptr_mask = torch.zeros([dim0,total_index],dtype=torch.double).to(self.device)
-        need_ptr_mask[:, ptrs_begin : ptrs_begin + ptrs_num] = 1
-        mask[:,:] = torch.where(need_ptrs,need_ptr_mask,mask)
-
-        # last operator is ptr, next should not still ptr
-        mask[:, ptrs_begin:ptrs_begin + ptrs_num] = torch.where(last_is_prts, 0.,
-                                                                   mask[:, ptrs_begin:ptrs_begin + ptrs_num])
-        # if need parameter or cant be a full alg(choose,update,search), mask ptr
-        need_para = (need_para_num != 0) | (operator_num<3)
-        mask[:, ptrs_begin:ptrs_begin + ptrs_num] = torch.where(need_para, 0.,
-                                                                mask[:, ptrs_begin:ptrs_begin + ptrs_num])
-        # force choose end
-        # make sure last operator is ptrs,and last normal operator is not crossover, then can choose 'end'
-        need_end = (operator_num >=8) * (need_para_num == 0) * last_is_prts * (~last_normal_is_cross)
-        need_end_mask = torch.zeros([dim0,total_index],dtype=torch.double).to(self.device)
-        need_end_mask[:,end_index] = 1
-        mask[:,:] = torch.where(need_end,need_end_mask,mask)
-
-        # mask pso
-        if discrete ==False:
-            mask[:,5] = 0.
-
-        # mask global search operator
-        need_mask_opera = (have_gs == 1)*(need_para_num == 0)
-        mask[:,only_gs_opera_index] = torch.where(need_mask_opera,0.,mask[:,only_gs_opera_index])
-        # mask global search parameter
-        need_mask_para = (have_gs == 1)*(need_para_num != 0)
-        dim0,dim1 = need_mask_para.shape
-        for i in range(dim0):
-            if ~need_mask_para[i,0]: continue
-            if discrete==False:
-                if last_opera_index[i] == 6 or last_opera_index[i] == 7 or last_opera_index[i] == 8:
-                    mask[i,31:] = 0
-                elif last_opera_index[i] == 13 or last_opera_index[i] == 17:
-                    mask[i, 31:] = 0
-                elif last_opera_index[i] == 16:
-                    if need_para_num[i,0] == 2:
-                        mask[i, 31:] = 0
-                    elif need_para_num[i,0] == 1:
-                        mask[i, 28:31] = 0
-            else:
-                if last_opera_index[i] == 6 or last_opera_index[i] == 7 or last_opera_index[i] == 9:
-                    mask[i, 21:27] = 0
-
-        if ~torch.sum(mask, dim=1).all():
-            print("have all zeros row!")
-        return mask
-
-    def judge_GS(self, have_gs, need_para_num, last_opera_index, trg):
-        # judge alg have Global Search or not
-        need_judge = (have_gs == 0) * (need_para_num == 0) * (last_opera_index >= search_begin) * (
-                    last_opera_index <= search_begin + search_num - 1)
-        dim0,dim1 = need_judge.shape
-        for i in range(dim0):
-            if ~need_judge[i,0]:continue
-            last_opera_index_i = last_opera_index[i, 0]
-            if only_gs_opera_index.__contains__(last_opera_index_i):
-                have_gs[i, 0] = 1
-            else:
-                para_num = operator_para[voc_list[last_opera_index_i]]
-
-                if discrete==False:
-                    if para_num == 1:
-                        para_index = trg[i, -1: ]
-                        have_gs[i, 0] = ~is_ls_para(last_opera_index_i,para_index) + 0
-                    elif para_num == 2:
-                        para_index_1 = trg[i, -2:-1]
-                        para_index_2 = trg[i, -1:]
-                        one = ~is_ls_para(last_opera_index_i, para_index_1) + 0
-                        two = ~is_ls_para(last_opera_index_i, para_index_2,2) + 0
-                        have_gs[i, 0] = one|two + 0
-                else:
-                    if para_num == 1:
-                        para_index = trg[i, -1: ]
-                        have_gs[i, 0] = ~dis_is_ls_opera(last_opera_index_i,para_index) + 0
-
-        return  have_gs
-
-    def choose_unfinished(self,finished,have_gs,last_opera_index,last_normal_opera_index,operator_num,need_para_num,trg,action_emb,enc_src):
-        unfinished_index = torch.where(finished == 0)[0]
-        last_opera_index = torch.index_select(last_opera_index, 0, unfinished_index)
-        last_normal_opera_index = torch.index_select(last_normal_opera_index, 0, unfinished_index)
-        operator_num = torch.index_select(operator_num, 0, unfinished_index)
-        need_para_num = torch.index_select(need_para_num, 0, unfinished_index)
-        unfinished_trg = torch.index_select(trg, 0, unfinished_index)
-        unfinished_have_gs = torch.index_select(have_gs, 0, unfinished_index)
-        unfinished_action_emb = torch.index_select(action_emb, 0, unfinished_index)
-        unfinished_enc_src = torch.index_select(enc_src, 0, unfinished_index)
-        return unfinished_index,last_opera_index,last_normal_opera_index,operator_num,need_para_num,unfinished_trg,unfinished_have_gs,unfinished_action_emb,unfinished_enc_src
-
-    def update_cur_alg_info(self,finished,unfinished_index,last_opera_index,last_normal_opera_index,operator_num,need_para_num,p,log_p,action_index,action_p,action_log_p,trg):
-        # update info of cur alg
-        is_opera = ((action_index >= operators_begin) * (action_index <= operators_begin + operators_num - 1)) | ((action_index >= ptrs_begin) * (action_index <= ptrs_begin + ptrs_num - 1))
-        last_opera_index = torch.tensor(where(is_opera.cpu(), action_index.cpu(), last_opera_index.cpu())).to(
-            self.device)
-        # operator without ptrs
-        is_normal_opera = ((action_index >= operators_begin) * (action_index <= operators_begin + operators_num - 1))
-        last_normal_opera_index = torch.tensor(where(is_normal_opera.cpu(), action_index.cpu(), last_normal_opera_index.cpu())).to(
-            self.device)
-        operator_num = torch.tensor(where(is_opera.cpu(), operator_num.cpu() + 1, operator_num.cpu())).to(self.device)
-        dim0, dim1 = is_opera.shape
-        for i in range(dim0):
-            if is_opera[i, 0]:
-                need_para_num[i, 0] = operator_para[voc_list[action_index[i, 0]]]
-
-        is_para = (action_index >= para_begin) * (action_index <= para_begin + para_num - 1)
-        need_para_num = torch.tensor(where(is_para.cpu(), need_para_num.cpu() - 1, need_para_num.cpu())).to(
-            self.device)
-
-
-        # cat finished and unfinished rows to keep matrix
-        cat_last_opera_index = torch.zeros(finished.shape, dtype=torch.int64).to(self.device)
-        cat_last_opera_index.scatter_(0, torch.unsqueeze(unfinished_index, 1), last_opera_index)
-        cat_last_normal_opera_index = torch.zeros(finished.shape, dtype=torch.int64).to(self.device)
-        cat_last_normal_opera_index.scatter_(0, torch.unsqueeze(unfinished_index, 1), last_normal_opera_index)
-        cat_operator_num = torch.zeros(finished.shape, dtype=torch.int64).to(self.device)
-        cat_operator_num.scatter_(0, torch.unsqueeze(unfinished_index, 1), operator_num)
-        cat_need_para_num = torch.zeros(finished.shape, dtype=torch.int64).to(self.device)
-        cat_need_para_num.scatter_(0, torch.unsqueeze(unfinished_index, 1), need_para_num)
-        last_opera_index = cat_last_opera_index
-        last_normal_opera_index = cat_last_normal_opera_index
-        operator_num = cat_operator_num
-        need_para_num = cat_need_para_num
-
-        # cat finished and unfinished rows to keep matrix
-        cat_p = torch.ones(finished.shape).to(self.device)
-        cat_p.scatter_(0, torch.unsqueeze(unfinished_index, 1), p)
-        cat_log_p = torch.zeros(finished.shape).to(self.device)
-        cat_log_p.scatter_(0, torch.unsqueeze(unfinished_index, 1), log_p)
-        cat_action_index = torch.zeros(finished.shape, dtype=torch.int64).to(self.device)
-        cat_action_index[:, :] = end_index
-        cat_action_index.scatter_(0, torch.unsqueeze(unfinished_index, 1), action_index)
-
-        action_p.append(cat_p)
-        action_log_p.append(cat_log_p)
-        trg = torch.cat([trg, cat_action_index], dim=1)
-
-        finished = torch.tensor(where(cat_action_index.cpu() == end_index, 1, finished.cpu())).to(self.device)
-
-        return last_opera_index,last_normal_opera_index,operator_num,need_para_num,action_p,action_log_p,trg,finished
-
-
+        rows = [allowed_next_tokens(row.detach().cpu().numpy()) for row in cur_alg]
+        return torch.as_tensor(np.stack(rows), dtype=torch.bool, device=self.device)
diff --git a/models/model/encoder.py b/models/model/encoder.py
deleted file mode 100644
index 0070a55..0000000
--- a/models/model/encoder.py
+++ /dev/null
@@ -1,33 +0,0 @@
-from torch import nn
-
-from models.blocks.encoder_layer import EncoderLayer
-from models.embedding.transformer_embedding import TransformerEmbedding
-
-
-class Encoder(nn.Module):
-
-    def __init__(self, enc_voc_size, max_len, d_model, ffn_hidden, n_head, n_layers, drop_prob, device):
-        super().__init__()
-        n_layers = 1 # force layers =1
-        self.emb = TransformerEmbedding(d_model=d_model,
-                                        max_len=max_len,
-                                        vocab_size=enc_voc_size,
-                                        drop_prob=drop_prob,
-                                        device=device)
-
-        self.layers = nn.ModuleList([EncoderLayer(d_model=d_model,
-                                                  ffn_hidden=ffn_hidden,
-                                                  n_head=n_head,
-                                                  drop_prob=drop_prob)
-                                     for _ in range(n_layers)])
-
-    def forward(self, x, src_mask):
-
-        # no need of emb
-        x = self.emb(x)
-
-        # add a dim here,because emb(x) will add dim and we dont need that
-        for layer in self.layers:
-            x = layer(x, src_mask)
-
-        return x
\ No newline at end of file
diff --git a/models/model/transformer.py b/models/model/transformer.py
index fbd0496..46d4492 100644
--- a/models/model/transformer.py
+++ b/models/model/transformer.py
@@ -1,57 +1,55 @@
-import torch
 from torch import nn
 
 from models.model.decoder import Decoder
-from models.model.encoder import Encoder
 
 
 class Transformer(nn.Module):
-
-    def __init__(self, src_pad_idx, trg_pad_idx, trg_sos_idx, enc_voc_size, dec_voc_size, d_model, n_head, max_len,
-                 ffn_hidden, n_layers, drop_prob, device):
+    """Decoder-only Transformer policy used by ALDes."""
+
+    def __init__(
+        self,
+        dec_voc_size,
+        d_model,
+        n_head,
+        max_len,
+        ffn_hidden,
+        n_layers,
+        drop_prob,
+        device,
+        condition_on_features=False,
+    ):
         super().__init__()
-        self.src_pad_idx = src_pad_idx
-        self.trg_pad_idx = trg_pad_idx
-        self.trg_sos_idx = trg_sos_idx
         self.device = device
-        self.encoder = Encoder(d_model=d_model,
-                               n_head=n_head,
-                               max_len=max_len,
-                               ffn_hidden=ffn_hidden,
-                               enc_voc_size=enc_voc_size,
-                               drop_prob=drop_prob,
-                               n_layers=n_layers,
-                               device=device)
-
-        self.decoder = Decoder(d_model=d_model,
-                               n_head=n_head,
-                               max_len=max_len,
-                               ffn_hidden=ffn_hidden,
-                               dec_voc_size=dec_voc_size,
-                               drop_prob=drop_prob,
-                               n_layers=n_layers,
-                               device=device)
-
-    def forward(self, src, trg, action_emb,action = None,reference = False):
-        action_emb_mask = None
-        action_emb = self.encoder(action_emb, action_emb_mask)
-
-        src_mask = None
-        trg_mask = None
-
-        enc_src = src
-        output = self.decoder(trg, enc_src, action_emb,trg_mask, src_mask,action,reference)
-        return output
-
-    def make_src_mask(self, src):
-        src_mask = (src != self.src_pad_idx).unsqueeze(1).unsqueeze(2)
-
-        # here dont need pading,once we input one vector of  proble repesent
-        src_mask[:,:,:,:] =1
-        return src_mask
-
-    def make_trg_mask(self, trg):
-        trg_len = trg.shape[1]
-        trg_sub_mask = torch.tril(torch.ones(trg_len, trg_len)).bool().to(self.device)
-        trg_mask =  trg_sub_mask
-        return trg_mask
\ No newline at end of file
+        self.decoder = Decoder(
+            d_model=d_model,
+            n_head=n_head,
+            max_len=max_len,
+            ffn_hidden=ffn_hidden,
+            dec_voc_size=dec_voc_size,
+            drop_prob=drop_prob,
+            n_layers=n_layers,
+            device=device,
+            condition_on_features=condition_on_features,
+        )
+
+    def forward(
+        self,
+        features,
+        target,
+        attention_positions,
+        action=None,
+        reference=False,
+    ):
+        # ``attention_positions`` is retained in the public call signature for
+        # compatibility with the original ALDes training loop. The decoder
+        # derives positional information internally.
+        del attention_positions
+        return self.decoder(
+            target,
+            features,
+            None,
+            None,
+            None,
+            action,
+            reference,
+        )
diff --git a/pflacco_feature.py b/pflacco_feature.py
index afaa6f4..6865dcd 100644
--- a/pflacco_feature.py
+++ b/pflacco_feature.py
@@ -1,111 +1,136 @@
-from datetime import datetime
+"""Paper-style landscape features used only by continual ALDes training."""
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Iterable
+
+import numpy as np
 import pandas as pd
-from pflacco.classical_ela_features import *
-from pflacco.sampling import create_initial_sample
-from ioh import get_problem, ProblemType
-import ioh
-from sklearn.preprocessing import StandardScaler
-
-
-def test_pflacco():
-    features = []
-    # Get all 24 single-objective noiseless BBOB function in dimension 2 and 3 for the first five instances.
-    for fid in range(1,25):
-        for dim in [2, 3]:
-            for iid in range(1, 6):
-                # Get optimization problem
-                #problem = get_problem(fid, iid, dim, problem_type = ProblemType.BBOB)
-                problem = ioh.get_problem(
-                    fid,
-                    instance=iid,
-                    dimension=dim,
-                    problem_class=ioh.ProblemClass.BBOB
-                )
-                # Create sample
-                X = create_initial_sample(dim, lower_bound = -5, upper_bound = 5)
-                y = X.apply(lambda x: problem(x), axis = 1)
-
-                # Calculate ELA features
-                ela_meta = calculate_ela_meta(X, y)
-                ela_distr = calculate_ela_distribution(X, y)
-                ela_level = calculate_ela_level(X, y)
-                nbc = calculate_nbc(X, y)
-                disp = calculate_dispersion(X, y)
-                ic = calculate_information_content(X, y, seed = 100)
-
-                # Store results in pandas dataframe
-                data = pd.DataFrame({**ic, **ela_meta, **ela_distr, **nbc, **disp, **{'fid': fid}, **{'dim': dim}, **{'iid': iid}}, index = [0])
-                features.append(data)
-
-    features = pd.concat(features).reset_index(drop = True)
-
-    print(features)
-
-# input solution: x-y-x-y-...
+
+from autooptlib.aldes import extract_pbo_features
+
+ELA_DIR = Path("ela")
+
+
 def cal_feature(solution):
-    for i in range(0,len(solution),2):
-        x = pd.DataFrame(np.array(solution[i])[0:10,:])
-        y= (np.array(solution[i+1])[0:10,:]).squeeze()
-        ela_meta = calculate_ela_meta(x, y)
-    return ela_meta
-
-        # x:10*200--》60s
-        #   10*400-->1088s
-
-
-def cal_PBO_feature(problem_id,instance,dim,seed):
-    print("\nbegin_{0}_{1}_{2}_{3}_".format(problem_id,instance,dim,seed)+ datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
-    X = np.random.randint(0, 2, [10*dim,dim])
-    problem = ioh.get_problem(
-        problem_id,
-        instance=instance,
-        dimension=dim,
-        problem_class=ioh.ProblemClass.BBOB
+    """Retain the small compatibility helper used by older experiments."""
+
+    from pflacco.classical_ela_features import calculate_ela_meta
+
+    result = None
+    for index in range(0, len(solution), 2):
+        decisions = pd.DataFrame(np.asarray(solution[index])[:10, :])
+        objectives = np.asarray(solution[index + 1])[:10].squeeze()
+        result = calculate_ela_meta(decisions, objectives)
+    return result
+
+
+def save_ela(
+    problem_ids: Iterable[int] = range(1, 24),
+    *,
+    seed: int = 1,
+) -> None:
+    """Extract and persist continual-task features and reusable populations."""
+
+    ELA_DIR.mkdir(parents=True, exist_ok=True)
+    dimensions = {1: 100, 2: 225, 3: 400}
+    for problem_id in problem_ids:
+        results = {
+            instance: extract_pbo_features(
+                int(problem_id),
+                instance=1,
+                dimension=dimension,
+                trials=5,
+                sample_factor=100,
+                feature_dim=32,
+                population_size=50,
+                seed=seed + 100 * int(problem_id) + instance,
+            )
+            for instance, dimension in dimensions.items()
+        }
+        result = results[1]
+        if any(item.feature_names != result.feature_names for item in results.values()):
+            raise ValueError("PBO dimensions produced incompatible feature schemas.")
+        averaged_features = np.mean(
+            np.vstack([item.features for item in results.values()]), axis=0
+        )
+        pd.DataFrame([averaged_features], columns=result.feature_names).to_csv(
+            ELA_DIR / f"ela_result{problem_id}.csv", index=False
+        )
+        np.savez_compressed(
+            ELA_DIR / f"initial_population{problem_id}.npz",
+            **{
+                f"instance_{instance - 1}": item.initial_populations
+                for instance, item in results.items()
+            },
+        )
+
+
+def transform(*, auto_generate: bool = True) -> np.ndarray:
+    """Load and standardize the 23 continual-task feature vectors."""
+
+    paths = [ELA_DIR / f"ela_result{problem}.csv" for problem in range(1, 24)]
+    missing = [path for path in paths if not path.exists()]
+    if missing:
+        if not auto_generate:
+            raise FileNotFoundError(f"Missing ALDes feature file: {missing[0]}")
+        save_ela()
+    frames = [pd.read_csv(path) for path in paths]
+    columns = list(frames[0].columns)
+    if any(list(frame.columns) != columns for frame in frames[1:]):
+        raise ValueError("Saved ALDes feature files do not share one schema.")
+    matrix = np.vstack([frame.iloc[0].to_numpy(dtype=float) for frame in frames])
+    mean = matrix.mean(axis=0)
+    scale = matrix.std(axis=0)
+    scale[scale == 0] = 1.0
+    return ((matrix - mean) / scale).astype(np.float32)
+
+
+def load_standardized_features(
+    problem_ids: Iterable[int], *, auto_generate: bool = True
+) -> dict[int, np.ndarray]:
+    """Fit feature scaling on only the continual tasks seen so far."""
+
+    ids = list(dict.fromkeys(int(problem_id) for problem_id in problem_ids))
+    if not ids:
+        raise ValueError("At least one seen problem is required.")
+    paths = {problem_id: ELA_DIR / f"ela_result{problem_id}.csv" for problem_id in ids}
+    missing_ids = [
+        problem_id for problem_id, path in paths.items() if not path.exists()
+    ]
+    if missing_ids:
+        if not auto_generate:
+            raise FileNotFoundError(f"Missing {paths[missing_ids[0]]}")
+        save_ela(missing_ids)
+    frames = {problem_id: pd.read_csv(path) for problem_id, path in paths.items()}
+    columns = list(frames[ids[0]].columns)
+    if any(list(frame.columns) != columns for frame in frames.values()):
+        raise ValueError("Saved ALDes feature files do not share one schema.")
+    matrix = np.vstack(
+        [frames[problem_id].iloc[0].to_numpy(dtype=float) for problem_id in ids]
     )
-    Y = problem(X)
-
-    x = pd.DataFrame(X)
-    y = (np.array(Y)).squeeze()
-    nbc = calculate_nbc(x, y)
-    ic = calculate_information_content(x, y, seed=100)
-    ela_meta = calculate_ela_meta(x, y)
-    #ela_distr = calculate_ela_distribution(x, y)
-    #ela_level = calculate_ela_level(x, y)
-
-    disp = calculate_dispersion(x, y,dist_method = 'hamming')
-
-    data = pd.DataFrame({**ic, **ela_meta, **nbc, **disp, **{'fid': problem_id}, **{'dim': dim}, **{'iid': instance}, **{'seed': seed}},
-                        index=[0])
-    #print(data)
-    print("end" + datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
-    return data
-#test_pflacco()
-def save_ela():
-    medians = []
-    for problem in range(1,24,1):
-        features = []
-        for dim in [100]:
-            for seed in range(1,6,1):
-                data = cal_PBO_feature(problem,1,dim,seed)
-                features.append(data)
-
-        features = pd.concat(features).reset_index(drop = True)
-        median = pd.DataFrame(features.median())
-        median.T.to_csv("ela/ela_result{0}.csv".format(problem))
-    #medians = pd.concat(medians).reset_index(drop=True)
-    #medians.to_csv("ela_result.csv")
-
-
-
-def transform():
-    elas = []
-    for problem in range(1,24,1):
-        ela = pd.read_csv("ela/ela_result{0}.csv".format(problem), index_col=0)
-        elas.append(ela)
-    elas = pd.concat(elas).reset_index(drop=True)
-
-    scaler = StandardScaler()
-    X_train = scaler.fit_transform(elas)
-
-    feature = X_train[:,:-4]
-    return feature
+    mean = matrix.mean(axis=0)
+    scale = matrix.std(axis=0)
+    scale[scale == 0] = 1.0
+    standardized = ((matrix - mean) / scale).astype(np.float32)
+    return {problem_id: standardized[index] for index, problem_id in enumerate(ids)}
+
+
+def load_initial_populations(problem_id: int) -> dict[int, np.ndarray]:
+    """Load the five sampled populations associated with one feature vector."""
+
+    path = ELA_DIR / f"initial_population{int(problem_id)}.npz"
+    if not path.exists():
+        raise FileNotFoundError(
+            f"Missing {path}; run pflacco_feature.py for continual training first."
+        )
+    with np.load(path, allow_pickle=False) as payload:
+        return {
+            int(name.rsplit("_", 1)[1]): np.array(payload[name], copy=True)
+            for name in payload.files
+        }
+
+
+if __name__ == "__main__":
+    save_ela()
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..73978d2
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,71 @@
+[build-system]
+requires = ["setuptools>=77", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "aldes"
+version = "2.0.0"
+description = "Pure-Python ALDes for automated metaheuristic algorithm design"
+readme = "README.md"
+requires-python = ">=3.9,<3.12"
+license = "Apache-2.0"
+license-files = ["LICENSE"]
+authors = [
+  { name = "Qi Zhao" },
+  { name = "Tengfei Liu" },
+  { name = "Bai Yan" },
+  { name = "Qiqi Duan" },
+  { name = "Jian Yang" },
+  { name = "Yuhui Shi" },
+]
+dependencies = [
+  "autooptlib[aldes] @ git+https://github.com/auto4opt/AutoOptLib.git@v1.3.0",
+  "numpy>=1.23,<3",
+  "scipy>=1.9,<2",
+]
+
+[project.optional-dependencies]
+test = [
+  "build>=1.2,<2",
+  "pytest>=8,<9",
+  "ruff>=0.9,<1",
+  "twine>=5,<7",
+]
+plot = [
+  "jupyter>=1,<2",
+  "matplotlib>=3.7,<4",
+  "seaborn>=0.12,<1",
+]
+
+[project.scripts]
+aldes-train = "train:main"
+aldes-paper-subset = "run_paper_subset:main"
+
+[project.urls]
+Paper = "https://doi.org/10.1109/TEVC.2024.3464677"
+Repository = "https://github.com/auto4opt/ALDes"
+Issues = "https://github.com/auto4opt/ALDes/issues"
+
+[tool.setuptools]
+py-modules = [
+  "EWC",
+  "aldes_setting",
+  "conf",
+  "pflacco_feature",
+  "run_paper_subset",
+  "train",
+]
+
+[tool.setuptools.packages.find]
+where = ["."]
+include = ["draw*", "models*", "util*"]
+
+[tool.setuptools.package-data]
+"draw.datas.reference_results" = ["**/*.csv", "**/*.mat", "**/*.pkl", "**/*.xlsx"]
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+
+[tool.ruff]
+target-version = "py39"
+extend-exclude = ["draw/*.ipynb"]
diff --git a/run_design_algs.py b/run_design_algs.py
deleted file mode 100644
index d77a993..0000000
--- a/run_design_algs.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import os
-
-import matlab.engine
-import matlab
-
-import numpy as np
-import re
-import ray
-import pickle
-from concurrent.futures import ThreadPoolExecutor, as_completed
-import time
-import pandas as pd
-
-eng = matlab.engine.start_matlab()
-# 此地址为test.m文件存放的地址
-work_path = os.getcwd() + "\\matlab"
-eng.cd(work_path)
-
-def run(alg):
-    action = alg.action
-    problem_id = alg.problem_id
-    seed = alg.seed
-    save_path = alg.save_path
-    # delet first one "Begin"
-    action.pop(0)
-    # delet "End"
-    while (action[len(action) - 1] == 17):
-        action.pop()  # delet last one
-
-    alg  = matlab.double(initializer=action)
-
-    # default eval setting
-    instances = matlab.double([4])
-    eval = 1
-
-    [performance, change,solution] = eng.get_per(alg, problem_id, instances, eval, nargout=3)
-
-    performance = np.array(performance)
-    performance = performance[0]
-
-    dump_file = save_path + f'seed{seed}_problem{problem_id}_res.pkl'
-    with open(dump_file, 'wb') as f:
-        # 使用pickle.dump()将字典对象序列化并保存到文件中
-        pickle.dump(performance, f)
-    return performance
-
-class Alg:
-    def __init__(self, action, problem_id, seed, path):
-        self.action = action
-        self.problem_id = problem_id
-        self.seed = seed
-        self.save_path = path
-    
-def read_algs(path, seeds, problem_set):
-    total_algs = []
-    pattern = re.compile(r'\d+\.\d+|\d+')
-
-    log_file = path + 'log.txt' 
-    with open(log_file, 'r', encoding='utf-8') as f:
-        keyword = 'train over action:'
-        lines = f.readlines()
-
-        index = 0
-        for seed in seeds:
-            for problem_id in problem_set:
-                while index < len(lines):
-                    line = lines[index]
-                    if keyword in line:
-                        index += 1
-                        numbers_of_alg = pattern.findall(lines[index])
-                        # convert str list to int list
-                        numbers_of_alg = list(map(int, numbers_of_alg))
-
-                        alg = Alg(numbers_of_alg, problem_id,seed,path)
-                        total_algs.append(alg)
-                        break
-                    index += 1 
- 
-    return total_algs
-
-# ray sames not suit with matlab
-def ray_parral():
-    num_workers = 8
-    while True:
-        num_runs_left = len(total_algs)
-        num_processes = min(num_workers, num_runs_left)
-
-        total_works = []
-        for _ in range(num_processes):
-            alg = total_algs.pop()
-            total_works.append(run.remote(alg.alg, alg.problem_id, alg.seed, save_path))
-
-        # collect results
-        outputs = ray.get(total_works)
-    
-
-def run_parral(seeds, problem_set, path):
-    seeds = [1, 2, 3, 4, 5]
-    problem_set = [1, 3, 14, 15, 17, 20]
-
-    total_algs = read_algs(path, seeds, problem_set)
-
-    #total_algs = total_algs[-6:] # run seed5 algs
-    # 创建一个包含3个线程的线程池
-    with ThreadPoolExecutor(max_workers=6) as executor:
-        # 提交任务到线程池
-        futures = [executor.submit(run, alg) for alg in total_algs]
-
-        # 使用 as_completed 方法获取任务结果
-        for future in as_completed(futures):
-            result = future.result()
-            print(result)
-
-def load_pkls(path, seeds, problem_set):        
-    df = pd.DataFrame(columns=['Problem', 'seed', 'dim', 'Mean', 'Variance'])
-    df.to_csv(path + 'result.csv', index=False)
-
-    for problem in problem_set:
-        datas = []
-        for seed in seeds:
-            file_name = f'seed{seed}_problem{problem}_res.pkl'
-            file_path = path + file_name
-
-            with open(file_path, 'rb') as f:
-                # loaded_data: steps(100)*algs(16)*instance(3)*runs(5)
-                data = pickle.load(f)
-                datas.append(data)
-
-        datas = np.array(datas)
-        mean = -datas.mean()
-        var = datas.var()
-
-        df = df._append({
-            'Problem': problem,
-            'seed': seed,
-            'dim': 625,
-            'Mean': mean,
-            'Variance': var
-        }, ignore_index=True)
-        df.to_csv(path + 'result.csv', index=False)
-if __name__ == '__main__':
-
-    seeds = [1, 2, 3, 4, 5]
-    seeds = [1]
-    problem_set = [1, 3, 14, 15, 17, 20]
-    FE3000_path = 'D:\\01Code\\ALDes\\draw\\datas\\pkls\\3000FE\\'
-    FE10000_path = 'D:\\01Code\\ALDes\\draw\\datas\\pkls\\10000FE\\'
-
-    #run_parral(seeds, problem_set,FE3000_path)
-
-    load_pkls(FE3000_path, seeds, problem_set)
-
-
diff --git a/run_paper_subset.py b/run_paper_subset.py
new file mode 100644
index 0000000..deb9730
--- /dev/null
+++ b/run_paper_subset.py
@@ -0,0 +1,223 @@
+"""Run a time-bounded subset of the published ALDes PBO experiment.
+
+Every completed training trial keeps the paper's per-trial protocol intact.
+The wall-clock budget only determines whether another whole problem is started.
+"""
+
+from __future__ import annotations
+
+import argparse
+from importlib.resources import files
+import json
+import os
+import platform
+import statistics
+import time
+from datetime import datetime
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import torch
+from scipy.io import loadmat
+from scipy.stats import mannwhitneyu
+
+import train as aldes_train
+from aldes_setting import begin_index
+from autooptlib.aldes.vocabulary import TOKEN_BY_INDEX, normalize_sequence
+from conf import batch_size_src, clip, device, ppo_epoch, total_epoch
+from util.device import describe_device
+from util.my_util import seed_torch
+
+
+DEFAULT_PROBLEMS = (1, 14, 15)
+DEFAULT_REFERENCE_DIR = Path(
+    str(files("draw.datas.reference_results").joinpath("design", "instance_4"))
+)
+
+
+def _parse_problems(value: str) -> list[int]:
+    problems = [int(item.strip()) for item in value.split(",") if item.strip()]
+    if not problems or any(problem < 1 or problem > 23 for problem in problems):
+        raise argparse.ArgumentTypeError(
+            "problems must be comma-separated IDs in 1..23"
+        )
+    return problems
+
+
+def _json_default(value: Any) -> Any:
+    if isinstance(value, (np.integer, np.floating)):
+        return value.item()
+    if isinstance(value, np.ndarray):
+        return value.tolist()
+    if isinstance(value, Path):
+        return str(value)
+    raise TypeError(f"Cannot serialize {type(value).__name__}")
+
+
+def _write_json(path: Path, payload: dict[str, Any]) -> None:
+    temporary = path.with_suffix(path.suffix + ".tmp")
+    temporary.write_text(
+        json.dumps(payload, indent=2, default=_json_default) + "\n",
+        encoding="utf-8",
+    )
+    temporary.replace(path)
+
+
+def _reference_result(reference_dir: Path, problem_id: int) -> np.ndarray:
+    source = reference_dir / f"f{problem_id}.mat"
+    values = np.asarray(loadmat(source)["res"], dtype=float).reshape(-1)
+    # AutoOptLib minimizes the negated IOH objective; the paper reports the
+    # original maximization orientation.
+    return -values
+
+
+def _summary(values: np.ndarray) -> dict[str, float | int]:
+    return {
+        "runs": int(values.size),
+        "mean": float(np.mean(values)),
+        "sample_variance": float(np.var(values, ddof=1)),
+        "sample_std": float(np.std(values, ddof=1)),
+        "minimum": float(np.min(values)),
+        "maximum": float(np.max(values)),
+    }
+
+
+def _model_inputs() -> tuple[None, torch.Tensor, torch.Tensor]:
+    target = torch.tensor([[begin_index]], device=device).repeat(batch_size_src, 1)
+    attention = torch.arange(0, 27, dtype=torch.int, device=device)
+    attention = attention.unsqueeze(0).repeat(batch_size_src, 1)
+    return None, target, attention
+
+
+def _run_trial(
+    problem_id: int,
+    seed: int,
+    reference_dir: Path,
+) -> dict[str, Any]:
+    started = time.monotonic()
+    aldes_train.evaluation_round = 0
+    aldes_train.logs.seed = seed
+    aldes_train.logs.problem_id = problem_id
+    seed_torch(seed)
+
+    model, optimizer = aldes_train.get_model("single")
+    source, target, attention = _model_inputs()
+    action = aldes_train.train(
+        model, optimizer, clip, problem_id, source, target, attention
+    )
+
+    test_mean, test_values = aldes_train.get_performance(action, problem_id, eval=1)
+    del test_mean
+    local = -np.asarray(test_values[0], dtype=float).reshape(-1)
+    reference = _reference_result(reference_dir, problem_id)
+    local_summary = _summary(local)
+    reference_summary = _summary(reference)
+    mean_gap = float(local_summary["mean"] - reference_summary["mean"])
+    relative_gap = mean_gap / max(abs(float(reference_summary["mean"])), 1e-12)
+    test = mannwhitneyu(local, reference, alternative="two-sided")
+
+    normalized_action = normalize_sequence(action.detach().cpu().numpy()[0])
+    elapsed = time.monotonic() - started
+    return {
+        "problem_id": problem_id,
+        "seed": seed,
+        "elapsed_seconds": elapsed,
+        "action_tokens": normalized_action,
+        "action_names": [TOKEN_BY_INDEX[token].name for token in normalized_action],
+        "local_values": local,
+        "local": local_summary,
+        "paper_reference_values": reference,
+        "paper_reference": reference_summary,
+        "mean_gap_local_minus_paper": mean_gap,
+        "relative_mean_gap": relative_gap,
+        "mann_whitney_u": float(test.statistic),
+        "mann_whitney_p": float(test.pvalue),
+    }
+
+
+def main(argv: list[str] | None = None) -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "--problems",
+        type=_parse_problems,
+        default=list(DEFAULT_PROBLEMS),
+        help="comma-separated PBO IDs (default: 1,14,15)",
+    )
+    parser.add_argument("--seed", type=int, default=1)
+    parser.add_argument("--time-budget-minutes", type=float, default=60.0)
+    parser.add_argument(
+        "--output-dir",
+        type=Path,
+        default=None,
+        help="default: experiments/paper_subset_<timestamp>",
+    )
+    parser.add_argument(
+        "--reference-dir",
+        type=Path,
+        default=DEFAULT_REFERENCE_DIR,
+    )
+    args = parser.parse_args(argv)
+
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    output_dir = args.output_dir or Path("experiments") / f"paper_subset_{timestamp}"
+    output_dir.mkdir(parents=True, exist_ok=False)
+    reference_dir = args.reference_dir
+    budget_seconds = args.time_budget_minutes * 60.0
+    experiment_started = time.monotonic()
+
+    payload: dict[str, Any] = {
+        "status": "running",
+        "created_at": datetime.now().astimezone().isoformat(),
+        "requested_problems": args.problems,
+        "seed": args.seed,
+        "time_budget_seconds": budget_seconds,
+        "protocol": {
+            "training_epochs": total_epoch,
+            "algorithms_per_epoch": batch_size_src,
+            "ppo_updates_per_epoch": ppo_epoch,
+            "training_instances": [1, 2, 3],
+            "training_runs_per_instance": 5,
+            "training_evaluations_per_run": 5_000,
+            "test_instance": 4,
+            "test_runs": 30,
+            "test_evaluations_per_run": 50_000,
+            "population_size": 50,
+        },
+        "environment": {
+            "python": platform.python_version(),
+            "torch": torch.__version__,
+            "platform": platform.platform(),
+            "machine": platform.machine(),
+            "cpu_count": os.cpu_count(),
+            "evaluation_workers": os.environ.get("ALDES_EVAL_WORKERS", "auto"),
+            "training_device": describe_device(device),
+        },
+        "paper_reference_dir": os.path.relpath(reference_dir, Path.cwd()),
+        "results": [],
+        "skipped_problems": [],
+    }
+    results_path = output_dir / "results.json"
+    _write_json(results_path, payload)
+
+    durations: list[float] = []
+    for index, problem_id in enumerate(args.problems):
+        elapsed = time.monotonic() - experiment_started
+        remaining = budget_seconds - elapsed
+        if durations and remaining < statistics.median(durations):
+            payload["skipped_problems"] = args.problems[index:]
+            break
+        result = _run_trial(problem_id, args.seed, reference_dir)
+        durations.append(float(result["elapsed_seconds"]))
+        payload["results"].append(result)
+        payload["elapsed_seconds"] = time.monotonic() - experiment_started
+        _write_json(results_path, payload)
+
+    payload["elapsed_seconds"] = time.monotonic() - experiment_started
+    payload["status"] = "completed"
+    _write_json(results_path, payload)
+    print(f"Structured results: {results_path.resolve()}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tests/test_aldes.py b/tests/test_aldes.py
new file mode 100644
index 0000000..0d52f85
--- /dev/null
+++ b/tests/test_aldes.py
@@ -0,0 +1,224 @@
+from __future__ import annotations
+
+import numpy as np
+import pytest
+import torch
+
+import conf
+import run_paper_subset
+import train as training_script
+from EWC import EWC
+from autooptlib.aldes import EvaluationConfig, validate_sequence
+from autooptlib.aldes.evaluator import (
+    _evaluate_pbo_sequences,
+    _resolve_evaluation_workers,
+)
+from models.model.transformer import Transformer
+from util import device as device_module
+
+
+def _model(*, continual: bool = False) -> Transformer:
+    return Transformer(
+        dec_voc_size=32,
+        d_model=32,
+        n_head=4,
+        max_len=50,
+        ffn_hidden=64,
+        n_layers=1,
+        drop_prob=0.0,
+        device=torch.device("cpu"),
+        condition_on_features=continual,
+    )
+
+
+def _inputs(batch_size: int = 4):
+    target = torch.full((batch_size, 1), 17, dtype=torch.long)
+    attention = torch.arange(27).repeat(batch_size, 1)
+    return target, attention
+
+
+def test_device_selection_prioritizes_accelerators(monkeypatch):
+    monkeypatch.setattr(device_module.torch.cuda, "is_available", lambda: True)
+    monkeypatch.setattr(device_module, "_mps_available", lambda: True)
+    assert device_module.resolve_device().type == "cuda"
+
+    monkeypatch.setattr(device_module.torch.cuda, "is_available", lambda: False)
+    assert device_module.resolve_device().type == "mps"
+
+    monkeypatch.setattr(device_module, "_mps_available", lambda: False)
+    assert device_module.resolve_device().type == "cpu"
+
+
+def test_cli_defaults_to_one_problem_and_one_seed():
+    args = training_script.build_parser().parse_args([])
+    assert args.mode == "single"
+    assert args.problems is None
+    assert args.seeds == [1]
+
+
+def test_cli_rejects_unknown_pbo_problem():
+    with pytest.raises(SystemExit):
+        training_script.build_parser().parse_args(["--problems", "24"])
+
+
+def test_paper_reference_data_is_packaged_and_loadable():
+    reference_dir = run_paper_subset.DEFAULT_REFERENCE_DIR
+    assert reference_dir.is_dir()
+    assert run_paper_subset._reference_result(reference_dir, 1).size == 30
+
+
+def test_main_uses_safe_single_problem_default(monkeypatch):
+    calls = []
+    training_script.EWC_ = object()
+    training_script.current_initial_populations = object()
+    monkeypatch.setattr(training_script, "seed_torch", lambda seed: None)
+    monkeypatch.setattr(
+        training_script,
+        "train_separately",
+        lambda problems, evaluate_test=False: calls.append((problems, evaluate_test)),
+    )
+
+    training_script.main([])
+
+    assert calls == [([1], False)]
+    assert training_script.EWC_ is None
+    assert training_script.current_initial_populations is None
+
+
+def test_unavailable_explicit_accelerator_fails_clearly(monkeypatch):
+    monkeypatch.setattr(device_module.torch.cuda, "is_available", lambda: False)
+    monkeypatch.setattr(device_module, "_mps_available", lambda: False)
+    with pytest.raises(RuntimeError, match="cannot access"):
+        device_module.resolve_device("cuda")
+    with pytest.raises(RuntimeError, match="does not provide"):
+        device_module.resolve_device("mps")
+
+
+def test_cpu_evaluation_worker_selection(monkeypatch):
+    monkeypatch.delenv("ALDES_EVAL_WORKERS", raising=False)
+    monkeypatch.setattr("autooptlib.aldes.evaluator.os.cpu_count", lambda: 10)
+    assert _resolve_evaluation_workers(None, 16) == 10
+    assert _resolve_evaluation_workers(20, 4) == 4
+    assert _resolve_evaluation_workers(1, 16) == 1
+    monkeypatch.setenv("ALDES_EVAL_WORKERS", "2")
+    assert _resolve_evaluation_workers(None, 16) == 2
+    with pytest.raises(ValueError, match="positive integer"):
+        _resolve_evaluation_workers(0, 4)
+
+
+def test_parallel_cpu_evaluation_matches_serial():
+    actions = np.asarray(
+        [
+            [17, 0, 29, 8, 29, 12, 29, 18],
+            [17, 1, 29, 11, 29, 12, 29, 18],
+        ]
+    )
+    config = EvaluationConfig(
+        population_size=4,
+        evaluations=20,
+        runs=2,
+        seed=19,
+    )
+    serial = _evaluate_pbo_sequences(actions, 1, [1], config, workers=1)
+    parallel = _evaluate_pbo_sequences(actions, 1, [1], config, workers=2)
+
+    np.testing.assert_array_equal(serial[0], parallel[0])
+    for serial_values, parallel_values in zip(serial[1], parallel[1]):
+        np.testing.assert_array_equal(serial_values, parallel_values)
+
+
+def test_single_problem_mode_is_default_and_ignores_features():
+    assert conf.aldes_mode == "single"
+    model = _model()
+    assert model.decoder.emb.tok_emb.padding_idx is None
+    target, attention = _inputs()
+
+    model.eval()
+    with torch.no_grad():
+        actions, _, sampled_log_probability = model(None, target, attention)
+    _, _, replay_log_probability = model(None, target, attention, action=actions)
+
+    for sequence in actions:
+        validate_sequence(sequence.tolist())
+    torch.testing.assert_close(
+        sampled_log_probability, replay_log_probability, rtol=0, atol=1e-6
+    )
+
+
+def test_continual_mode_requires_and_uses_problem_features():
+    torch.manual_seed(4)
+    model = _model(continual=True)
+    target, attention = _inputs()
+    zeros = torch.zeros(4, 1, 32)
+    ones = torch.ones(4, 1, 32)
+
+    with pytest.raises(ValueError, match="requires problem features"):
+        model(None, target, attention)
+
+    model.eval()
+    with torch.no_grad():
+        actions, _, sampled_log_probability = model(zeros, target, attention)
+        _, _, replay_log_probability = model(zeros, target, attention, action=actions)
+        _, _, changed_log_probability = model(ones, target, attention, action=actions)
+
+    torch.testing.assert_close(
+        sampled_log_probability, replay_log_probability, rtol=0, atol=1e-6
+    )
+    assert not torch.equal(replay_log_probability, changed_log_probability)
+
+
+def test_ewc_fisher_and_penalty_are_finite():
+    model = _model()
+    target, attention = _inputs()
+    actions, _, log_probability = model(None, target, attention)
+    del actions
+    (-log_probability.sum(dim=1).mean()).backward()
+
+    ewc = EWC(model)
+    ewc.update_diag_fisher(model)
+    assert torch.isfinite(ewc.penalty(model))
+    assert all(
+        torch.isfinite(value).all() for value in ewc._precision_matrices.values()
+    )
+
+
+class _MemoryLog:
+    seed = 1
+    problem_id = 1
+
+    def write_log(self, _message):
+        pass
+
+    def dump_log(self, _experience):
+        pass
+
+
+def test_one_ppo_update_runs_without_matlab(monkeypatch):
+    torch.manual_seed(8)
+    model = _model()
+    optimizer = torch.optim.Adam(model.parameters(), lr=5e-5)
+    target, attention = _inputs()
+
+    def objective(actions, _problem_id):
+        costs = np.asarray(actions.detach().sum(dim=1), dtype=float)
+        return costs.tolist(), [np.asarray([cost]) for cost in costs]
+
+    monkeypatch.setattr(training_script, "logs", _MemoryLog())
+    monkeypatch.setattr(training_script, "get_performance", objective)
+    monkeypatch.setattr(training_script, "EWC_", None)
+    inferred = training_script.PPO(
+        model,
+        optimizer,
+        clip=1.0,
+        total_epoch=1,
+        ppo_epoch=1,
+        baseline=None,
+        clip_coef=0.2,
+        src=None,
+        trg=target,
+        att_src=attention,
+        problem_id=1,
+    )
+
+    assert inferred.shape[0] == 1
+    validate_sequence(inferred[0].tolist())
diff --git a/train.py b/train.py
index 3e6aab4..bbb1c1d 100644
--- a/train.py
+++ b/train.py
@@ -1,27 +1,48 @@
-import os
+import argparse
 import time
+from pathlib import Path
+
+import torch
+from torch import nn
 from torch.optim import Adam
 
-from data import *
+from aldes_setting import begin_index, total_index
+from conf import (
+    adam_eps,
+    aldes_mode,
+    batch_size_src,
+    clip,
+    continual_problem_sets,
+    d_model,
+    device,
+    drop_prob,
+    ewc_weight,
+    ffn_hidden,
+    init_lr,
+    max_len,
+    n_heads,
+    n_layers,
+    ppo_epoch,
+    total_epoch,
+    use_ewc,
+    weight_decay,
+)
+from EWC import EWC
 from models.model.transformer import Transformer
-from util.bleu import idx_to_word, get_bleu
 
-import matlab.engine
-import matlab
-from matlab_setting import *
-from EWC import *
-from pflacco_feature import cal_feature, transform
-from util.my_util import *
+from autooptlib.aldes import evaluate_pbo_actions
+from pflacco_feature import (
+    load_initial_populations,
+    load_standardized_features,
+)
+from util.device import describe_device
+from util.my_util import RunLogger, seed_torch
 
-useEWC = False
 EWC_ = None
+current_initial_populations = None
+evaluation_round = 0
 
-eng = matlab.engine.start_matlab()
-# matlab root dir
-work_path = os.getcwd() + "\\matlab"
-eng.cd(work_path)
-
-logs = my_log()
+logs = RunLogger()
 
 
 def count_parameters(model):
@@ -29,96 +50,106 @@ def count_parameters(model):
 
 
 def initialize_weights(m):
-    if hasattr(m, 'weight') and m.weight.dim() > 1:
-        nn.init.kaiming_uniform(m.weight.data)
-
-
-def get_model():
-    if model_type == 0:
-        model = Transformer(src_pad_idx=src_pad_idx,
-                            trg_pad_idx=trg_pad_idx,
-                            trg_sos_idx=trg_sos_idx,
-                            d_model=d_model,
-                            enc_voc_size=total_index,  # enc_voc_size,
-                            dec_voc_size=total_index,  # voc_size,
-                            max_len=max_len,
-                            ffn_hidden=ffn_hidden,
-                            n_head=n_heads,
-                            n_layers=n_layers,
-                            drop_prob=drop_prob,
-                            device=device).to(device)
-
-        print(f'The model has {count_parameters(model):,} trainable parameters')
-        model.apply(initialize_weights)
-        optimizer = Adam(params=model.parameters(),
-                         lr=init_lr,
-                         weight_decay=weight_decay,
-                         eps=adam_eps)
-    else:
-        pass
+    if hasattr(m, "weight") and m.weight is not None and m.weight.dim() > 1:
+        nn.init.kaiming_uniform_(m.weight.data)
+
+
+def get_model(mode=None):
+    mode = aldes_mode if mode is None else str(mode).lower()
+    if mode not in {"single", "continual"}:
+        raise ValueError("ALDes mode must be 'single' or 'continual'.")
+    model = Transformer(
+        d_model=d_model,
+        dec_voc_size=total_index,
+        max_len=max_len,
+        ffn_hidden=ffn_hidden,
+        n_head=n_heads,
+        n_layers=n_layers,
+        drop_prob=drop_prob,
+        device=device,
+        condition_on_features=(mode == "continual"),
+    ).to(device)
+
+    print(
+        f"The model has {count_parameters(model):,} trainable parameters "
+        f"on {describe_device(device)}"
+    )
+    model.apply(initialize_weights)
+    optimizer = Adam(
+        params=model.parameters(),
+        lr=init_lr,
+        weight_decay=weight_decay,
+        eps=adam_eps,
+    )
     return model, optimizer
 
 
 def get_performance(action, problem_id, eval=0):
-    # call matlab funtion get performance
-    mean_performances = []
-    performances = []
+    """Evaluate generated actions with the Python AutoOptLib backend."""
+    global evaluation_round
+    evaluation_seed = None
+    if logs.seed >= 0:
+        evaluation_seed = int(logs.seed) * 1_000_000 + evaluation_round
+        evaluation_round += 1
+    mean_performances, performances = evaluate_pbo_actions(
+        action,
+        problem_id,
+        evaluate_test=bool(eval),
+        seed=evaluation_seed,
+        initial_populations=current_initial_populations,
+        workers=None,
+    )
     if eval == 1:
-        action = action[0:3]
-    for j in range(action.shape[0]):
-        # get performance and reward
-        alg_list = action[j, :].reshape(-1).tolist()
-
-        # delet first one "Begin"
-        alg_list.pop(0)
-        # delet "End"
-        while (alg_list[len(alg_list) - 1] == end_index):
-            alg_list.pop()  # delet last one
-        # print(alg_list)
-        alg = matlab.double(initializer=alg_list)
-        if eval == 0:
-            instances = matlab.double([1, 2, 3])
-        else:
-            instances = matlab.double([4])
-        performance = eng.get_perf(alg, problem_id, instances, eval, nargout=1)
-
-        if eval == 0:
-            performance = np.array(performance)
-            performance = performance[0:3]
-        else:
-            performance = np.array(performance)
-            performance = performance[0]
-        # performance = np.array(performance) #first row is instanceTrain , second is instanceTest, do not use Test
-        mean_performance = performance.mean()
-        mean_performances.append(mean_performance)
-        performances.append(performance)
-        if eval == 1:
-            logs.write_log("problem_" + (problem_id).__str__() + " result:\n" + str(performance))
-            logs.write_log("problem_" + (problem_id).__str__() + " result mean:\n" + str(mean_performance))
+        for mean_performance, performance in zip(mean_performances, performances):
+            logs.write_log(
+                "problem_" + str(problem_id) + " result:\n" + str(performance)
+            )
+            logs.write_log(
+                "problem_" + str(problem_id) + " result mean:\n" + str(mean_performance)
+            )
 
     return mean_performances, performances
 
 
-def PPO(model, optimizer, clip, total_epoch, ppo_epoch, baseline, clip_coef, src, trg, att_src, problem_id):
+def PPO(
+    model,
+    optimizer,
+    clip,
+    total_epoch,
+    ppo_epoch,
+    baseline,
+    clip_coef,
+    src,
+    trg,
+    att_src,
+    problem_id,
+):
     ewc_loss = None
     global EWC_
     action_total_list = []
     log_performances = []
     # ppo
     for i in range(total_epoch):
+        progress = i / max(1, total_epoch - 1)
+        learning_rate = init_lr * (1.0 - progress)
+        for group in optimizer.param_groups:
+            group["lr"] = learning_rate
         since = time.time()
+        model.eval()
         with torch.no_grad():
             action, action_p, action_log_p = model(src, trg, att_src)
             action_log_p = torch.squeeze(action_log_p, 2)
             logs.write_log("action in train: \n" + str(action[0:5]))
-            logs.write_log("action_p in train: \n " + str(torch.squeeze(action_p[0:5], 2)))
+            logs.write_log(
+                "action_p in train: \n " + str(torch.squeeze(action_p[0:5], 2))
+            )
             action_total_list += action.tolist()
 
         mean_performances, performances = get_performance(action, problem_id)
         log_performances.append(performances)
         logs.write_log("performances in train: \n " + str(performances[0:5]))
-        mean_performances = torch.tensor(mean_performances)
-        cost = mean_performances.to(device)
+        model_device = next(model.parameters()).device
+        cost = torch.as_tensor(mean_performances, device=model_device)
         if baseline is None:
             baseline = cost.mean()
         else:
@@ -127,19 +158,20 @@ def PPO(model, optimizer, clip, total_epoch, ppo_epoch, baseline, clip_coef, src
 
         # ppo update
         for j in range(ppo_epoch):
-
-            _, new_action_p, new_action_log_p = model(src, trg, att_src, action)
+            _, _, new_action_log_p = model(src, trg, att_src, action)
             new_action_log_p = torch.squeeze(new_action_log_p, 2)
             logratio = new_action_log_p.sum(1) - action_log_p.sum(1)
             ratio = logratio.exp()
 
             pg_loss1 = (cost - baseline) * ratio
-            pg_loss2 = (cost - baseline) * torch.clamp(ratio, 1 - clip_coef, 1 + clip_coef)
+            pg_loss2 = (cost - baseline) * torch.clamp(
+                ratio, 1 - clip_coef, 1 + clip_coef
+            )
             loss = torch.max(pg_loss1, pg_loss2).mean()
 
             if EWC_ is not None:
                 ewc_loss = EWC_.penalty(model)
-                loss += ewc_loss
+                loss += ewc_weight * ewc_loss
             optimizer.zero_grad()
             loss.backward()
 
@@ -147,225 +179,323 @@ def PPO(model, optimizer, clip, total_epoch, ppo_epoch, baseline, clip_coef, src
             optimizer.step()
 
         if ewc_loss is not None:
-            logs.write_log(('step :', round((i / total_epoch) * 100, 2),
-                            '% , ewc_loss :', ewc_loss.item()).__str__())
-            print('step :', round((i / total_epoch) * 100, 2),
-                  '% , ewc_loss :', ewc_loss.item())
+            logs.write_log(
+                (
+                    "step :",
+                    round((i / total_epoch) * 100, 2),
+                    "% , ewc_loss :",
+                    ewc_loss.item(),
+                ).__str__()
+            )
+            print(
+                "step :",
+                round((i / total_epoch) * 100, 2),
+                "% , ewc_loss :",
+                ewc_loss.item(),
+            )
 
         time_elapsed = time.time() - since
 
-        print('step :', round((i / total_epoch) * 100, 2),
-              '% , loss :', loss.item(),
-              ', cost_mean :', cost.mean().item(),
-              ', baseline :', baseline.item(),
-              ',Training complete in {:.0f}m {:.0f}s'.format(
-                  time_elapsed // 60, time_elapsed % 60)
-              )
-        logs.write_log(('step :', round((i / total_epoch) * 100, 2),
-                        '% , loss :', loss.item(),
-                        ', cost_mean :', cost.mean().item(),
-                        ', baseline :', baseline.item(),
-                        ',Training complete in {:.0f}m {:.0f}s'.format(
-                            time_elapsed // 60, time_elapsed % 60)
-                        ).__str__())
+        print(
+            "step :",
+            round((i / total_epoch) * 100, 2),
+            "% , loss :",
+            loss.item(),
+            ", cost_mean :",
+            cost.mean().item(),
+            ", baseline :",
+            baseline.item(),
+            ",Training complete in {:.0f}m {:.0f}s".format(
+                time_elapsed // 60, time_elapsed % 60
+            ),
+        )
+        logs.write_log(
+            (
+                "step :",
+                round((i / total_epoch) * 100, 2),
+                "% , loss :",
+                loss.item(),
+                ", cost_mean :",
+                cost.mean().item(),
+                ", baseline :",
+                baseline.item(),
+                ",Training complete in {:.0f}m {:.0f}s".format(
+                    time_elapsed // 60, time_elapsed % 60
+                ),
+            ).__str__()
+        )
 
     logs.dump_log(log_performances)
+    model.eval()
+    with torch.no_grad():
+        inferred_action, _, _ = model(src, trg, att_src, reference=True)
+    return inferred_action[:1]
+
+
+def PPO_get_ewc(
+    model,
+    optimizer,
+    clip,
+    total_epoch,
+    ppo_epoch,
+    baseline,
+    clip_coef,
+    src,
+    trg,
+    att_src,
+    problem_id,
+):
+    del clip, total_epoch, ppo_epoch
+    model.eval()
+    with torch.no_grad():
+        action, _, action_log_p = model(src, trg, att_src)
+        action_log_p = torch.squeeze(action_log_p, 2)
+
+    mean_performances, _ = get_performance(action, problem_id)
+    model_device = next(model.parameters()).device
+    cost = torch.as_tensor(mean_performances, device=model_device)
+    if baseline is None:
+        baseline = cost.mean()
+    else:
+        baseline = 0.8 * baseline + 0.2 * cost.mean()
+    baseline = baseline.detach()
 
-    return action
-
-
-def PPO_get_ewc(model, optimizer, clip, total_epoch, ppo_epoch, baseline, clip_coef, src, trg, att_src, problem_id):
-    # ppo
-    for i in range(1):
-        since = time.time()
-        with torch.no_grad():
-            action, action_p, action_log_p = model(src, trg, att_src)
-            action_log_p = torch.squeeze(action_log_p, 2)
-
-        mean_performances, performances = get_performance(action, problem_id)
-        mean_performances = torch.tensor(mean_performances)
-        cost = mean_performances.to(device)
-        if baseline is None:
-            baseline = cost.mean()
-        else:
-            baseline = 0.8 * baseline + 0.2 * cost.mean()
-        baseline = baseline.detach()
-
-        # ppo update
-        for j in range(1):
-            _, new_action_p, new_action_log_p = model(src, trg, att_src, action)
-            new_action_log_p = torch.squeeze(new_action_log_p, 2)
-            logratio = new_action_log_p.sum(1) - action_log_p.sum(1)
-            ratio = logratio.exp()
+    _, _, new_action_log_p = model(src, trg, att_src, action)
+    new_action_log_p = torch.squeeze(new_action_log_p, 2)
+    logratio = new_action_log_p.sum(1) - action_log_p.sum(1)
+    ratio = logratio.exp()
 
-            pg_loss1 = (cost - baseline) * ratio
-            pg_loss2 = (cost - baseline) * torch.clamp(ratio, 1 - clip_coef, 1 + clip_coef)
-            loss = torch.max(pg_loss1, pg_loss2).mean()
+    pg_loss1 = (cost - baseline) * ratio
+    pg_loss2 = (cost - baseline) * torch.clamp(ratio, 1 - clip_coef, 1 + clip_coef)
+    loss = torch.max(pg_loss1, pg_loss2).mean()
 
-            optimizer.zero_grad()
-            loss.backward()
-            EWC_.update_diag_fisher(model)
+    optimizer.zero_grad()
+    loss.backward()
+    EWC_.update_diag_fisher(model)
     return action
 
 
 def train(model, optimizer, clip, problem_id, src, trg, att_src):
-    model.train()
+    # PPO old/new policy likelihoods must use the same deterministic network
+    # mode; gradients still propagate while the module is in eval mode.
+    model.eval()
     baseline = None
     clip_coef = 0.2
-    if train_type == 0:
-        action = PPO(model, optimizer, clip, total_epoch, ppo_epoch, baseline, clip_coef, src, trg, att_src, problem_id)
-
-    return action
+    return PPO(
+        model,
+        optimizer,
+        clip,
+        total_epoch,
+        ppo_epoch,
+        baseline,
+        clip_coef,
+        src,
+        trg,
+        att_src,
+        problem_id,
+    )
+
+
+def train_separately(problem_ids=None, *, evaluate_test=False):
+    """Train independent policies for one or more PBO problems.
+
+    The default is intentionally one problem. Single-problem design returns
+    the inferred action directly and does not need a model checkpoint.
+    """
 
-
-def evaluate(model, iterator, criterion):
-    model.eval()
-    epoch_loss = 0
-    batch_bleu = []
-    with torch.no_grad():
-        for i, batch in enumerate(iterator):
-            src = batch.src
-            trg = batch.trg
-            output = model(src, trg[:, :-1])
-            output_reshape = output.contiguous().view(-1, output.shape[-1])
-            trg = trg[:, 1:].contiguous().view(-1)
-
-            loss = criterion(output_reshape, trg)
-            epoch_loss += loss.item()
-
-            total_bleu = []
-            for j in range(batch_size):
-                try:
-                    trg_words = idx_to_word(batch.trg[j], loader.target.vocab)
-                    output_words = output[j].max(dim=1)[1]
-                    output_words = idx_to_word(output_words, loader.target.vocab)
-                    bleu = get_bleu(hypotheses=output_words.split(), reference=trg_words.split())
-                    total_bleu.append(bleu)
-                except:
-                    pass
-
-            total_bleu = sum(total_bleu) / len(total_bleu)
-            batch_bleu.append(total_bleu)
-
-    batch_bleu = sum(batch_bleu) / len(batch_bleu)
-    return epoch_loss / len(iterator), batch_bleu
-
-
-def train_separately():
     print("Train separately")
     logs.write_log("Train separately")
-    problem_set = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21]
-    problem_nums = 0
-    input_src = []
+    problem_set = [1] if problem_ids is None else [int(item) for item in problem_ids]
+    actions = {}
     for problem_id in problem_set:
         logs.problem_id = problem_id
         print("Train problem_" + problem_id.__str__())
         logs.write_log("Train problem_" + problem_id.__str__())
 
-        model, optimizer = get_model()
+        model, optimizer = get_model("single")
 
-        src = torch.randn(10, d_model).to(device)  # 512->30
+        src = None
         trg = torch.tensor([begin_index]).to(device)
         trg = trg.unsqueeze(dim=0)
         # train with batch
-        src = src.repeat(batch_size_src, 1, 1)
         trg = trg.repeat(batch_size_src, 1)
-        att_src = torch.range(0, 26, 1, dtype=torch.int).to(device)
+        att_src = torch.arange(0, 27, 1, dtype=torch.int).to(device)
         att_src = att_src.unsqueeze(dim=0)
         att_src = att_src.repeat(batch_size_src, 1)
 
-        input_src.append(src)
         logs.write_log("model input :\n" + str(src))
 
         action = train(model, optimizer, clip, problem_id, src, trg, att_src)
-
+        actions[problem_id] = action.detach().cpu()
         logs.write_log("train over action: \n" + str(action))
+        print(f"problem_{problem_id} inferred action: {action[0].tolist()}")
 
-        # get_performance(action,problem_id,eval=1)
+        if evaluate_test:
+            get_performance(action, problem_id, eval=1)
+    return actions
 
 
-def train_in_one():
+def train_in_one(problem_sequence=None, *, checkpoint_dir=None):
+    """Train one feature-conditioned policy across a problem sequence."""
+
     print("Train In One")
     logs.write_log("Train In One")
     input_src = {}
-    model, optimizer = get_model()
-    global EWC_
-    problem_set = [1, 2, 11, 18, 19, 22, 23]
-    problem_nums = 0
-    feature = transform()
-    for problem_id in problem_set:
-        src = torch.from_numpy(feature[problem_id - 1][0:d_model].reshape(1, d_model)).to(torch.float32).to(device)
-        src = src.repeat(batch_size_src, 1, 1)
-
-        input_src[problem_id] = src
-    for problem_id in problem_set:
+    model, optimizer = get_model("continual")
+    global EWC_, current_initial_populations
+    if problem_sequence is None:
+        problem_sequence = [
+            problem_id
+            for problem_set in continual_problem_sets
+            for problem_id in problem_set
+        ]
+    else:
+        problem_sequence = [int(problem_id) for problem_id in problem_sequence]
+    seen_problem_ids = []
+    for stage, problem_id in enumerate(problem_sequence, start=1):
+        active_problem_ids = list(dict.fromkeys(seen_problem_ids + [problem_id]))
+        feature = load_standardized_features(active_problem_ids)
+        for active_id, vector in feature.items():
+            src = torch.from_numpy(vector[:d_model].reshape(1, d_model))
+            src = src.to(torch.float32).to(device)
+            input_src[active_id] = src.repeat(batch_size_src, 1, 1)
         logs.problem_id = problem_id
-        problem_nums += 1
         print("Train problem_" + problem_id.__str__())
         logs.write_log("Train problem_" + problem_id.__str__())
+        current_initial_populations = load_initial_populations(problem_id)
 
         trg = torch.tensor([begin_index]).to(device)
         trg = trg.unsqueeze(dim=0)
         # train with batch
         trg = trg.repeat(batch_size_src, 1)
-        att_src = torch.range(0, 26, 1, dtype=torch.int).to(device)
+        att_src = torch.arange(0, 27, 1, dtype=torch.int).to(device)
         att_src = att_src.unsqueeze(dim=0)
         att_src = att_src.repeat(batch_size_src, 1)
 
-        action = train(model, optimizer, clip, problem_id, input_src[problem_id], trg, att_src)
-
-        # logs.write_log("train over action: \n"+str(action))
-        if useEWC is True:
+        action = train(
+            model, optimizer, clip, problem_id, input_src[problem_id], trg, att_src
+        )
+        logs.write_log("train over action: \n" + str(action))
+        if use_ewc is True:
             print("Cal EWC______________________")
             EWC_ = EWC(model)
-            for i in problem_set:
+            fisher_problem_ids = list(dict.fromkeys(seen_problem_ids + [problem_id]))
+            for i in fisher_problem_ids:
+                current_initial_populations = load_initial_populations(i)
                 temp_src = input_src[i]
-                PPO_get_ewc(model, optimizer, clip, total_epoch, ppo_epoch, None, 0.2, temp_src, trg, att_src, i)
-                if i == problem_id: break
+                PPO_get_ewc(
+                    model,
+                    optimizer,
+                    clip,
+                    total_epoch,
+                    ppo_epoch,
+                    None,
+                    0.2,
+                    temp_src,
+                    trg,
+                    att_src,
+                    i,
+                )
             for key in EWC_._precision_matrices:
-                EWC_._precision_matrices[key] = EWC_._precision_matrices[key] / (problem_nums)
-
-        torch.save(model.state_dict(), 'logs/' + "train_in_one/" + problem_id.__str__() + '.pt')
+                EWC_._precision_matrices[key] = EWC_._precision_matrices[key] / len(
+                    fisher_problem_ids
+                )
+        seen_problem_ids.append(problem_id)
+
+        if checkpoint_dir is not None:
+            output = Path(checkpoint_dir)
+            output.mkdir(parents=True, exist_ok=True)
+            torch.save(
+                model.state_dict(),
+                output / f"stage{stage}_problem{problem_id}.pt",
+            )
 
         logs.write_log("EWC TEST : train over problem : " + (problem_id).__str__())
         for key, value in input_src.items():
-            old_action, action_p, action_log_p = model(input_src[key], trg, att_src, reference=True)
+            current_initial_populations = load_initial_populations(key)
+            old_action, _, _ = model(input_src[key], trg, att_src, reference=True)
             logs.write_log("problem_" + key.__str__() + " action:\n" + str(old_action))
             get_performance(old_action[0:1], key, eval=1)
-
-
-def test():
-    model, optimizer = get_model()
-    model.load_state_dict(
-        torch.load(r'D:\01Code\transformer-rl-3\logs\Transformer_PPO\100_32\1_11_17_12_11\train_in_one.pt'))
-    input_src = []
-    problem_set = [2, 3, 4, 5, 6, 7, 8, 9, 10]
-    problem_nums = 0
-    for problem_id in problem_set:
-        src = torch.randn(30, d_model).to(device)  # 512->30
-        trg = torch.tensor([begin_index]).to(device)
-        trg = trg.unsqueeze(dim=0)
-        # train with batch
-        src = src.repeat(batch_size_src, 1, 1)
-        trg = trg.repeat(batch_size_src, 1)
-
-        att_src = torch.range(0, 26, 1, dtype=torch.int).to(device)
-        att_src = att_src.unsqueeze(dim=0)
-        att_src = att_src.repeat(batch_size_src, 1)
-        input_src.append(src)
-
-        with torch.no_grad():
-            action, action_p, action_log_p = model(src, trg, att_src)
-            action_log_p = torch.squeeze(action_log_p, 2)
-
-    mean_performances, performances = get_performance(action, problem_id)
-    mean_performances = torch.tensor(mean_performances)
-
-
-if __name__ == '__main__':
-    for seed in range(1, 6, 1):
+    current_initial_populations = None
+
+
+def _id_list(value):
+    """Parse a comma-separated list of positive integer IDs."""
+
+    try:
+        values = [int(item.strip()) for item in value.split(",") if item.strip()]
+    except ValueError as exc:
+        raise argparse.ArgumentTypeError(
+            "IDs must be comma-separated integers."
+        ) from exc
+    if not values or any(item <= 0 for item in values):
+        raise argparse.ArgumentTypeError(
+            "At least one positive integer ID is required."
+        )
+    return values
+
+
+def _problem_list(value):
+    values = _id_list(value)
+    if any(item > 23 for item in values):
+        raise argparse.ArgumentTypeError("PBO problem IDs must be in 1..23.")
+    return values
+
+
+def build_parser():
+    parser = argparse.ArgumentParser(description="Train the pure-Python ALDes policy.")
+    parser.add_argument(
+        "--mode",
+        choices=("single", "continual"),
+        default=aldes_mode,
+        help="single designs from scratch for each problem; continual reuses one policy",
+    )
+    parser.add_argument(
+        "--problems",
+        type=_problem_list,
+        default=None,
+        help="comma-separated PBO IDs (single-mode default: 1)",
+    )
+    parser.add_argument(
+        "--seeds",
+        type=_id_list,
+        default=[1],
+        help="comma-separated training seeds (default: 1)",
+    )
+    parser.add_argument(
+        "--evaluate-test",
+        action="store_true",
+        help="run the paper's 30-run test after single-problem training",
+    )
+    parser.add_argument(
+        "--checkpoint-dir",
+        type=Path,
+        default=None,
+        help="optional continual-mode state-dictionary output directory",
+    )
+    return parser
+
+
+def main(argv=None):
+    """Command-line entry point with a safe one-problem, one-seed default."""
+
+    args = build_parser().parse_args(argv)
+    if args.mode == "single" and args.checkpoint_dir is not None:
+        raise SystemExit("--checkpoint-dir is only available in continual mode.")
+
+    global EWC_, current_initial_populations, evaluation_round
+    for seed in args.seeds:
+        EWC_ = None
+        current_initial_populations = None
+        evaluation_round = 0
         logs.seed = seed
-        logs.write_log('seed is {}'.format(seed))
+        logs.write_log(f"seed is {seed}")
         seed_torch(seed)
-        #train_in_one()
-        train_separately()
+        if args.mode == "continual":
+            train_in_one(args.problems, checkpoint_dir=args.checkpoint_dir)
+        else:
+            train_separately(args.problems or [1], evaluate_test=args.evaluate_test)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/util/__init__.py b/util/__init__.py
index cbea971..bb98588 100644
--- a/util/__init__.py
+++ b/util/__init__.py
@@ -2,4 +2,4 @@
 @author : Hyunwoong
 @when : 2019-10-28
 @homepage : https://github.com/gusdnd852
-"""
\ No newline at end of file
+"""
diff --git a/util/__pycache__/__init__.cpython-38.pyc b/util/__pycache__/__init__.cpython-38.pyc
deleted file mode 100644
index fa71b17..0000000
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deleted file mode 100644
index 5581fc4..0000000
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diff --git a/util/__pycache__/data_loader.cpython-38.pyc b/util/__pycache__/data_loader.cpython-38.pyc
deleted file mode 100644
index 3cc3dc7..0000000
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deleted file mode 100644
index 41dc7ec..0000000
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diff --git a/util/__pycache__/tokenizer.cpython-38.pyc b/util/__pycache__/tokenizer.cpython-38.pyc
deleted file mode 100644
index c558f43..0000000
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diff --git a/util/bleu.py b/util/bleu.py
deleted file mode 100644
index c1af8b8..0000000
--- a/util/bleu.py
+++ /dev/null
@@ -1,56 +0,0 @@
-"""
-@author : Hyunwoong
-@when : 2019-12-22
-@homepage : https://github.com/gusdnd852
-"""
-import math
-from collections import Counter
-
-import numpy as np
-
-
-def bleu_stats(hypothesis, reference):
-    """Compute statistics for BLEU."""
-    stats = []
-    stats.append(len(hypothesis))
-    stats.append(len(reference))
-    for n in range(1, 5):
-        s_ngrams = Counter(
-            [tuple(hypothesis[i:i + n]) for i in range(len(hypothesis) + 1 - n)]
-        )
-        r_ngrams = Counter(
-            [tuple(reference[i:i + n]) for i in range(len(reference) + 1 - n)]
-        )
-
-        stats.append(max([sum((s_ngrams & r_ngrams).values()), 0]))
-        stats.append(max([len(hypothesis) + 1 - n, 0]))
-    return stats
-
-
-def bleu(stats):
-    """Compute BLEU given n-gram statistics."""
-    if len(list(filter(lambda x: x == 0, stats))) > 0:
-        return 0
-    (c, r) = stats[:2]
-    log_bleu_prec = sum(
-        [math.log(float(x) / y) for x, y in zip(stats[2::2], stats[3::2])]
-    ) / 4.
-    return math.exp(min([0, 1 - float(r) / c]) + log_bleu_prec)
-
-
-def get_bleu(hypotheses, reference):
-    """Get validation BLEU score for dev set."""
-    stats = np.array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
-    for hyp, ref in zip(hypotheses, reference):
-        stats += np.array(bleu_stats(hyp, ref))
-    return 100 * bleu(stats)
-
-
-def idx_to_word(x, vocab):
-    words = []
-    for i in x:
-        word = vocab.itos[i]
-        if '<' not in word:
-            words.append(word)
-    words = " ".join(words)
-    return words
diff --git a/util/data_loader.py b/util/data_loader.py
deleted file mode 100644
index d91f9a3..0000000
--- a/util/data_loader.py
+++ /dev/null
@@ -1,47 +0,0 @@
-"""
-@author : Hyunwoong
-@when : 2019-10-29
-@homepage : https://github.com/gusdnd852
-"""
-from torchtext.legacy.data import Field, BucketIterator
-from torchtext.legacy.datasets.translation import Multi30k
-
-
-class DataLoader:
-    source: Field = None
-    target: Field = None
-
-    def __init__(self, ext, tokenize_en, tokenize_de, init_token, eos_token):
-        self.ext = ext
-        self.tokenize_en = tokenize_en
-        self.tokenize_de = tokenize_de
-        self.init_token = init_token
-        self.eos_token = eos_token
-        print('dataset initializing start')
-
-    def make_dataset(self):
-        if self.ext == ('.de', '.en'):
-            self.source = Field(tokenize=self.tokenize_de, init_token=self.init_token, eos_token=self.eos_token,
-                                lower=True, batch_first=True)
-            self.target = Field(tokenize=self.tokenize_en, init_token=self.init_token, eos_token=self.eos_token,
-                                lower=True, batch_first=True)
-
-        elif self.ext == ('.en', '.de'):
-            self.source = Field(tokenize=self.tokenize_en, init_token=self.init_token, eos_token=self.eos_token,
-                                lower=True, batch_first=True)
-            self.target = Field(tokenize=self.tokenize_de, init_token=self.init_token, eos_token=self.eos_token,
-                                lower=True, batch_first=True)
-
-        train_data, valid_data, test_data = Multi30k.splits(exts=self.ext, fields=(self.source, self.target))
-        return train_data, valid_data, test_data
-
-    def build_vocab(self, train_data, min_freq):
-        self.source.build_vocab(train_data, min_freq=min_freq)
-        self.target.build_vocab(train_data, min_freq=min_freq)
-
-    def make_iter(self, train, validate, test, batch_size, device):
-        train_iterator, valid_iterator, test_iterator = BucketIterator.splits((train, validate, test),
-                                                                              batch_size=batch_size,
-                                                                              device=device)
-        print('dataset initializing done')
-        return train_iterator, valid_iterator, test_iterator
diff --git a/util/device.py b/util/device.py
new file mode 100644
index 0000000..9df3aae
--- /dev/null
+++ b/util/device.py
@@ -0,0 +1,65 @@
+"""Portable PyTorch device selection for ALDes training."""
+
+from __future__ import annotations
+
+import torch
+
+
+def _mps_available() -> bool:
+    backend = getattr(torch.backends, "mps", None)
+    return bool(backend is not None and backend.is_available())
+
+
+def resolve_device(requested: str | torch.device = "auto") -> torch.device:
+    """Select CUDA/ROCm, Apple MPS, or CPU in that priority order.
+
+    PyTorch's ROCm build intentionally exposes AMD GPUs through the
+    ``torch.cuda`` API, so the CUDA branch covers both NVIDIA and AMD.
+    """
+
+    name = str(requested).strip().lower()
+    if name in {"", "auto"}:
+        if torch.cuda.is_available():
+            return torch.device("cuda:0")
+        if _mps_available():
+            return torch.device("mps")
+        return torch.device("cpu")
+
+    if name in {"amd", "rocm", "hip"}:
+        if not torch.cuda.is_available() or torch.version.hip is None:
+            raise RuntimeError(
+                "AMD GPU acceleration requires a ROCm-enabled PyTorch build."
+            )
+        return torch.device("cuda:0")
+
+    device = torch.device(name)
+    if device.type == "cuda" and not torch.cuda.is_available():
+        raise RuntimeError(
+            "CUDA/ROCm was requested, but this PyTorch installation cannot "
+            "access a compatible GPU."
+        )
+    if device.type == "mps" and not _mps_available():
+        raise RuntimeError(
+            "MPS was requested, but this PyTorch installation or Mac does "
+            "not provide the MPS backend."
+        )
+    if device.type not in {"cpu", "cuda", "mps"}:
+        raise ValueError("ALDES_DEVICE must be auto, cpu, cuda, cuda:N, mps, or rocm.")
+    return device
+
+
+def describe_device(device: torch.device) -> str:
+    """Return a concise human-readable accelerator description."""
+
+    if device.type == "cuda":
+        backend = "AMD ROCm" if torch.version.hip is not None else "NVIDIA CUDA"
+        index = (
+            device.index if device.index is not None else torch.cuda.current_device()
+        )
+        return f"{backend}: {torch.cuda.get_device_name(index)}"
+    if device.type == "mps":
+        return "Apple MPS"
+    return "CPU"
+
+
+__all__ = ["describe_device", "resolve_device"]
diff --git a/util/epoch_timer.py b/util/epoch_timer.py
deleted file mode 100644
index 314dab7..0000000
--- a/util/epoch_timer.py
+++ /dev/null
@@ -1,12 +0,0 @@
-"""
-@author : Hyunwoong
-@when : 2019-10-29
-@homepage : https://github.com/gusdnd852
-"""
-
-
-def epoch_time(start_time, end_time):
-    elapsed_time = end_time - start_time
-    elapsed_mins = int(elapsed_time / 60)
-    elapsed_secs = int(elapsed_time - (elapsed_mins * 60))
-    return elapsed_mins, elapsed_secs
diff --git a/util/my_util.py b/util/my_util.py
index ad81a9c..e543257 100644
--- a/util/my_util.py
+++ b/util/my_util.py
@@ -1,56 +1,52 @@
-import torch
-import torch.nn as nn
-import torch.optim as optim
-from torch.distributions.normal import Normal
+from __future__ import annotations
+
+import pickle
 from datetime import datetime
-import os
+from pathlib import Path
+from typing import Any
+
 import numpy as np
-import pickle
+import torch
+
 
-def _get_action(model_output):
-    # 偶数列
-    mean = model_output[:, 1::2]
-    # 奇数列
-    sdv = model_output[:, 0::2]
-    # here use exp make sure its positive
-    sdv = torch.exp(sdv)
-
-    probs = Normal(mean, sdv)
-    action = probs.sample()
-    action = torch.sigmoid(action)
-    return action, probs.log_prob(action).sum(1), probs.entropy().sum(1)
-
-def seed_torch(seed=2): #1029
-	np.random.seed(seed)
-	torch.manual_seed(seed)
-	torch.cuda.manual_seed(seed)
-	torch.cuda.manual_seed_all(seed) # if you are using multi-GPU.
-	torch.backends.cudnn.benchmark = False
-	torch.backends.cudnn.deterministic = True
-     
-class my_log():
-    def __init__(self):
-        now = datetime.now()  # 获得当前时间
-        global_timestr = now.strftime("%m_%d_%H_%M")
-        self.log_dir = f'logs/{global_timestr}/'
-        os.makedirs(self.log_dir)
-        self.log_file = self.log_dir + 'log.txt'
-        
+def seed_torch(seed: int = 2) -> None:
+    """Seed the random-number generators used by ALDes."""
+
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed(seed)
+        torch.cuda.manual_seed_all(seed)
+        torch.backends.cudnn.benchmark = False
+        torch.backends.cudnn.deterministic = True
+    mps = getattr(torch, "mps", None)
+    if mps is not None and torch.backends.mps.is_available():
+        mps.manual_seed(seed)
+
+
+class RunLogger:
+    """Write human-readable logs and raw PPO histories for one process."""
+
+    def __init__(self, root: str | Path = "logs") -> None:
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S_%f")
+        self.log_dir = Path(root) / timestamp
+        self.log_file = self.log_dir / "log.txt"
         self.problem_id = -1
         self.seed = -1
 
-    
-    def write_log(self,info):
-        f = open(self.log_file, 'a')
-        now = datetime.now()  # 获得当前时间
-        timestr = now.strftime("%m_%d_%H_%M")
-        f.write('\n' + timestr + ":"+info)
-        f.close()
-
-    def dump_log(self,experience):
-        dump_file = self.log_dir + f'seed{self.seed}_problem{self.problem_id}_.pkl'
-        with open(dump_file, 'wb') as f:
-            # 使用pickle.dump()将字典对象序列化并保存到文件中
-            pickle.dump(experience, f)
-         
+    def write_log(self, info: Any) -> None:
+        self.log_dir.mkdir(parents=True, exist_ok=True)
+        timestamp = datetime.now().isoformat(timespec="seconds")
+        with self.log_file.open("a", encoding="utf-8") as stream:
+            stream.write(f"\n{timestamp}: {info}")
+
+    def dump_log(self, experience: Any) -> None:
+        self.log_dir.mkdir(parents=True, exist_ok=True)
+        dump_file = self.log_dir / (
+            f"seed{self.seed}_problem{self.problem_id}_training.pkl"
+        )
+        with dump_file.open("wb") as stream:
+            pickle.dump(experience, stream)
+
 
+__all__ = ["RunLogger", "seed_torch"]
diff --git a/util/tokenizer.py b/util/tokenizer.py
deleted file mode 100644
index c71d578..0000000
--- a/util/tokenizer.py
+++ /dev/null
@@ -1,27 +0,0 @@
-"""
-@author : Hyunwoong
-@when : 2019-10-29
-@homepage : https://github.com/gusdnd852
-"""
-#import spacy
-
-
-class Tokenizer:
-
-    def __init__(self):
-        self.spacy_de = None
-        self.spacy_en = None
-        #self.spacy_de = spacy.load('de_core_news_sm')
-        #self.spacy_en = spacy.load('en_core_web_sm')
-
-    def tokenize_de(self, text):
-        """
-        Tokenizes German text from a string into a list of strings
-        """
-        return [tok.text for tok in self.spacy_de.tokenizer(text)]
-
-    def tokenize_en(self, text):
-        """
-        Tokenizes English text from a string into a list of strings
-        """
-        return [tok.text for tok in self.spacy_en.tokenizer(text)]