From b9daa76d08f5dc5e69f9aa229ef1fa66ecbfd9ab Mon Sep 17 00:00:00 2001 From: Qi Zhao Date: Mon, 20 Jul 2026 19:28:29 +0800 Subject: [PATCH 1/2] Release pure-Python ALDes 2.0.0 --- .DS_Store | Bin 6148 -> 0 bytes .github/workflows/ci.yml | 68 ++ .github/workflows/release.yml | 26 + .gitignore | 15 + CHANGELOG.md | 43 ++ CITATION.cff | 40 ++ EWC.py | 88 +-- README.md | 196 +++++- __pycache__/EWC.cpython-38.pyc | Bin 2467 -> 0 bytes __pycache__/conf.cpython-38.pyc | Bin 656 -> 0 bytes __pycache__/data.cpython-38.pyc | Bin 340 -> 0 bytes __pycache__/matlab_setting.cpython-38.pyc | Bin 2138 -> 0 bytes __pycache__/pflacco_test.cpython-38.pyc | Bin 3167 -> 0 bytes aldes.yaml | 135 +--- aldes_setting.py | 9 + conf.py | 39 +- data.py | 10 - draw/__init__.py | 1 + draw/bloxplot.ipynb | 8 +- draw/continue_problem.ipynb | 2 +- draw/convergence_curve.ipynb | 4 +- draw/datas/__init__.py | 1 + .../instance4_default_para/f1.mat | Bin .../instance4_default_para/f10.mat | Bin 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a8bebcf9bf2d6246ada5b40cc365d830766ba307..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6148 zcmeHLu}T9$5S>j92}zUEMz|=5S_o-UpAst@A-&NgqGHYq8d19|1ntz$(#GCT5K^d( z;0IU>f}r3B_-1#L?3$c*BC->9-)3iK=iN*0HX9-m^LvdVQJ#o0XpCMK-4J6xw*gD( znNgtNHJa(MN&(+@ z(H37?0tx8UpdB#XHyNfU9xA5K;G?tJ)13)Q|Nl9fK$LJ&{Ke) z4?Y@WpfOe`A06oA5di36S{lZA9sqrffyP)NJTReBfhtw#BZg4vnD=#DpfOgc(n;vU zhtQRUKA{L%9qaotoJ63|^-ck&fUQ7Uz0LFfzkqu<;rwqWxpz(hr@+5bKzQX!c^;Rf z_tt@n_yj&# B&}aYv diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..0414184 --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,68 @@ +name: CI + +on: + push: + 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. 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zEFMZ>N*o)aUxGO;+Jf0ME=CMv+?MW5Bq9}bZ$5Vcfyp$|8pYc?rhw#Qu6@H@I699pypAo5MP_!@^QOnbKmMHZ$fV7v-gSnfQW^Hpr48?DGb{+ff{tMPJWEZ zHr=`GOlch?e@rVb5wV`L?)V*b-|$IPCXa31*}-H=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.""" diff --git a/matlab/result/Discrete Simulated Annealing/instance4_default_para/f1.mat b/draw/datas/reference_results/Discrete Simulated Annealing/instance4_default_para/f1.mat similarity index 100% rename from matlab/result/Discrete Simulated Annealing/instance4_default_para/f1.mat rename to draw/datas/reference_results/Discrete Simulated Annealing/instance4_default_para/f1.mat diff --git a/matlab/result/Discrete Simulated Annealing/instance4_default_para/f10.mat b/draw/datas/reference_results/Discrete Simulated Annealing/instance4_default_para/f10.mat similarity index 100% rename from matlab/result/Discrete Simulated Annealing/instance4_default_para/f10.mat rename to draw/datas/reference_results/Discrete Simulated Annealing/instance4_default_para/f10.mat diff --git a/matlab/result/Discrete Simulated Annealing/instance4_default_para/f11.mat b/draw/datas/reference_results/Discrete Simulated Annealing/instance4_default_para/f11.mat similarity index 100% rename from matlab/result/Discrete Simulated Annealing/instance4_default_para/f11.mat rename to draw/datas/reference_results/Discrete Simulated Annealing/instance4_default_para/f11.mat diff --git a/matlab/result/Discrete Simulated Annealing/instance4_default_para/f12.mat b/draw/datas/reference_results/Discrete Simulated Annealing/instance4_default_para/f12.mat similarity index 100% rename from matlab/result/Discrete Simulated Annealing/instance4_default_para/f12.mat rename to draw/datas/reference_results/Discrete Simulated Annealing/instance4_default_para/f12.mat diff --git a/matlab/result/Discrete Simulated Annealing/instance4_default_para/f13.mat b/draw/datas/reference_results/Discrete Simulated Annealing/instance4_default_para/f13.mat similarity index 100% rename from matlab/result/Discrete Simulated Annealing/instance4_default_para/f13.mat rename to draw/datas/reference_results/Discrete Simulated Annealing/instance4_default_para/f13.mat diff --git a/matlab/result/Discrete Simulated Annealing/instance4_default_para/f14.mat b/draw/datas/reference_results/Discrete Simulated Annealing/instance4_default_para/f14.mat similarity index 100% rename from matlab/result/Discrete Simulated Annealing/instance4_default_para/f14.mat rename to draw/datas/reference_results/Discrete Simulated Annealing/instance4_default_para/f14.mat diff --git a/matlab/result/Discrete Simulated Annealing/instance4_default_para/f15.mat b/draw/datas/reference_results/Discrete Simulated Annealing/instance4_default_para/f15.mat similarity index 100% rename from matlab/result/Discrete Simulated Annealing/instance4_default_para/f15.mat rename to draw/datas/reference_results/Discrete Simulated Annealing/instance4_default_para/f15.mat diff --git a/matlab/result/Discrete Simulated Annealing/instance4_default_para/f16.mat b/draw/datas/reference_results/Discrete Simulated Annealing/instance4_default_para/f16.mat similarity index 100% rename from matlab/result/Discrete Simulated Annealing/instance4_default_para/f16.mat rename to draw/datas/reference_results/Discrete Simulated Annealing/instance4_default_para/f16.mat diff --git a/matlab/result/Discrete Simulated Annealing/instance4_default_para/f17.mat b/draw/datas/reference_results/Discrete Simulated Annealing/instance4_default_para/f17.mat similarity index 100% rename from matlab/result/Discrete Simulated Annealing/instance4_default_para/f17.mat rename to draw/datas/reference_results/Discrete Simulated Annealing/instance4_default_para/f17.mat diff --git 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0000000..f3cf6d4 --- /dev/null +++ b/draw/datas/reference_results/__init__.py @@ -0,0 +1 @@ +"""Historical paper results used for reproducibility comparisons.""" diff --git a/matlab/result/design/instance_1/f1.mat b/draw/datas/reference_results/design/instance_1/f1.mat similarity index 100% rename from matlab/result/design/instance_1/f1.mat rename to draw/datas/reference_results/design/instance_1/f1.mat diff --git a/matlab/result/design/instance_1/f10.mat b/draw/datas/reference_results/design/instance_1/f10.mat similarity index 100% rename from matlab/result/design/instance_1/f10.mat rename to draw/datas/reference_results/design/instance_1/f10.mat diff --git a/matlab/result/design/instance_1/f11.mat b/draw/datas/reference_results/design/instance_1/f11.mat similarity index 100% rename from matlab/result/design/instance_1/f11.mat rename to draw/datas/reference_results/design/instance_1/f11.mat diff --git a/matlab/result/design/instance_1/f12.mat 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b/draw/datas/reference_results/tabu_irace/instance_4/f3.pkl similarity index 100% rename from matlab/result/tabu_irace/instance_4/f3.pkl rename to draw/datas/reference_results/tabu_irace/instance_4/f3.pkl diff --git a/matlab/result/tabu_irace/instance_4/f4.pkl b/draw/datas/reference_results/tabu_irace/instance_4/f4.pkl similarity index 100% rename from matlab/result/tabu_irace/instance_4/f4.pkl rename to draw/datas/reference_results/tabu_irace/instance_4/f4.pkl diff --git a/matlab/result/tabu_irace/instance_4/f5.pkl b/draw/datas/reference_results/tabu_irace/instance_4/f5.pkl similarity index 100% rename from matlab/result/tabu_irace/instance_4/f5.pkl rename to draw/datas/reference_results/tabu_irace/instance_4/f5.pkl diff --git a/matlab/result/tabu_irace/instance_4/f6.pkl b/draw/datas/reference_results/tabu_irace/instance_4/f6.pkl similarity index 100% rename from matlab/result/tabu_irace/instance_4/f6.pkl rename to draw/datas/reference_results/tabu_irace/instance_4/f6.pkl diff --git a/matlab/result/tabu_irace/instance_4/f7.pkl b/draw/datas/reference_results/tabu_irace/instance_4/f7.pkl similarity index 100% rename from matlab/result/tabu_irace/instance_4/f7.pkl rename to draw/datas/reference_results/tabu_irace/instance_4/f7.pkl diff --git a/matlab/result/tabu_irace/instance_4/f8.pkl b/draw/datas/reference_results/tabu_irace/instance_4/f8.pkl similarity index 100% rename from matlab/result/tabu_irace/instance_4/f8.pkl rename to draw/datas/reference_results/tabu_irace/instance_4/f8.pkl diff --git a/matlab/result/tabu_irace/instance_4/f9.pkl b/draw/datas/reference_results/tabu_irace/instance_4/f9.pkl similarity index 100% rename from matlab/result/tabu_irace/instance_4/f9.pkl rename to draw/datas/reference_results/tabu_irace/instance_4/f9.pkl diff --git a/matlab/result/tabu_irace/instance_4/result.csv b/draw/datas/reference_results/tabu_irace/instance_4/result.csv similarity index 100% rename from matlab/result/tabu_irace/instance_4/result.csv rename to draw/datas/reference_results/tabu_irace/instance_4/result.csv diff --git a/matlab/result/tabu_irace/tabu_result.csv b/draw/datas/reference_results/tabu_irace/tabu_result.csv similarity index 100% rename from matlab/result/tabu_irace/tabu_result.csv rename to draw/datas/reference_results/tabu_irace/tabu_result.csv diff --git a/draw/diff_FE.ipynb b/draw/diff_FE.ipynb index 03f78f7..8f6a801 100644 --- a/draw/diff_FE.ipynb +++ b/draw/diff_FE.ipynb @@ -160,12 +160,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "result_pictures/diff_FE_fig3/F1.svg\n", - "result_pictures/diff_FE_fig3/F3.svg\n", - "result_pictures/diff_FE_fig3/F14.svg\n", - "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> - -% 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 . - -% Please read the documentation at 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 -% . -% ----------------------------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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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 1731a77bd3977088fe98915bc8b36e604d7ace17..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 24726 zcma%?V{j!t*ywk+cDLqfw{Ew#ZQHhO+uE&d+qR9}+ICNUs?)yvzxVxgKiy1zGsz^A zJef(JnFo;*Qk4}FCT62&B$g9Wr8Bp(HKQd~v^8=ycW}1jCRP;DkdkL-r6m?|HZyWH 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zi*T>6w2JbZ?SR%SXTvcI2_=c>CcHL?2+tEVGP8Ra9iR?Ah5~XG_dbH@x-lP3IzRU&^iAr==TOeB%A5nr%;QB#`(9P@q2Olp6tkw73O|OUIKKFACRp@4 zN75EfJVr8eg)PL9{#JB5P0Vgi-rrN;|CyCM{U^}#BNzHM9P9hkvA)j?edkclE_J;u zwf(u+$61-j%aVTjp_2cEejh9%(=Uri`{^K}6!Ur@zd+Wr?~wI9Mmpkppg$fCYR^9; zCd0mKX5{KT_+Q{o44i#FviE76`f zAR-Ir<@kh|OKiH4IGl`e6qGH;5#lJ!;|V0^9bh%@dfah-b-eT&-M75_nvw0j50rf_ zwBe(PW8o%sJnkw?!{?Y$WVJmmX7AAiyU?VB6J(JW#Km@XIpTQfKPg|KtUIUCeU9SG z8t+!)?L~OS$a_lrD0uuZwY4)fZo|Ii<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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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> - -% 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 deleted file mode 100644 index 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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> - -% 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 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zz$Ke??Gaui+=|PjNnVYhqRL9O!!ZT;mXf;_cINoD&T~(a)5cJR8*gK=n1Zk1*lzq= zM#Ma(7$?@1k#SOC#K)X&1@xF?!(vV+pqAX@UE8VHAG~kF9aRR@uy)WX*W3j0O#axu zSPk^B68{NXOT>7!Rwpj)??A(g3VQr*=)m5-ApJ`4bI{U>deO5UsIXoZ(JWl}tri~c zXFUeoC?IoQ((#&x+rusA^=OW}qFke$0F&Q!k7z3us@oNMf3kE0`j1RR(LJd6<@}_P zEO!#N#x>FHuS;ay=jiVu9g~dpTKu-A)<%b37?-;>teb~YmGD^5j0V*oyx`JaEChC@ zzi7{oMwBgknR4U-6?2sc1#zik=;X99Xuh)mu@q|63p7NT$S@Ewenr-Dfbcd{ulR7Wxf`L+;Naw ztkQr2t_4gI=1|=FOW(A)Y0(?hu}4fFKry*fVwq<GfZaqEpA5Z zTm}J~un3Nc4%VX)QIRBQ?i1of78n`5<0vL{Z@?k{DJb#IFbqwY;a@QZ`{FtMVDVtR zWWehKh!>lO%taayyKVKlP!=6eI0@&rgie7Oy^2q_F@nHz*wLFag|6ihJ6;P7==-pN zKwg~*ni{Jc`1N{Vay_ESP6RBVak{H4y%O?$Y`jyNs95(!fqT282ig)6libe|unn$B zvsZd4*s9FaLFsWi(BL^k{&hWQuX5{QX`33oPF*w>f7=A_A3pC^42&b^Y5xofpNiKy vuK+s-9a=u)lD5r}N;|MO@&2j2H08>tl`D 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> - -% 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; %Ĭ200 -Wroom=30; %Ĭ30 -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); %Խsum 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); %ĬϡֵԽ󣬴path lossԽ˥Խأsum 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 - -% 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 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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 = 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