<img src="./Doc/logo.png" width=256 height=256 />
# PlatEMO
[![](https://img.shields.io/badge/Download-Latest-yellow.svg)](https://github.com/BIMK/PlatEMO/archive/master.zip)
[![](https://img.shields.io/github/release/BIMK/PlatEMO.svg)](https://github.com/BIMK/PlatEMO/releases/)
[![](https://img.shields.io/badge/Matlab-%3E%3D%202014a%20-blue.svg)](#PlatEMO)
[![](https://img.shields.io/badge/Windows-Pass-brightgreen.svg)](#PlatEMO)
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[![](https://img.shields.io/badge/MacOS-Validating-red.svg)](#PlatEMO)
Evolutionary multi-objective optimization platform
* 100+ open source evolutionary algorithms
* 120+ open source multi-objective test problems
* Powerful GUI for performing experiments in parallel
* Generating results in the format of Excel or LaTeX table by one-click operation
* State-of-the-art algorithms will be included continuously
Thank you very much for using PlatEMO. The copyright of PlatEMO belongs to the BIMK Group. This
tool is mainly for research and educational purposes. The codes were implemented based on our
understanding of the algorithms published in the papers. You should not rely upon the material or
information on the website as a basis for making any business, legal or any other decisions. We
assume no responsibilities for any consequences of your using any algorithms in the tool. All
publications using the platform should acknowledge the use of “PlatEMO” and reference the
following literature:
## Copyright
> The Copyright of the PlatEMO belongs to the BIMK group. You are free to [use the PlatEMO](https://github.com/BIMK/PlatEMO/releases) for **research purposes**. All publications which use this platform or any code in the platform should **acknowledge the use of "PlatEMO" and reference** _"Ye Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum], IEEE Computational Intelligence Magazine, 2017, 12(4): 73-87"._
```
@article{PlatEMO,
title={{PlatEMO}: A {MATLAB} platform for evolutionary multi-objective optimization},
author={Tian, Ye and Cheng, Ran and Zhang, Xingyi and Jin, Yaochu},
journal={IEEE Computational Intelligence Magazine},
volume={12},
number={4},
pages={73--87},
year={2017},
}
```
# Release Highlights of PlatEMO 2.1
* Add the sparse multi-objective evolutionary algorithm SparseEA.
* Add the sparse multi-objective test suite SMOP1-SMOP8.
* Add four sparse multi-objective optimization problems, i.e., feature selection, pattern mining, critical node detection, and neural network training.
* Add the diversity metric CPF (i.e., coverage over Pareto front).
* Add the irregular multi-objective test suite IMOP1-IMOP8.
# Release Highlights of PlatEMO 2.0
* __Lighter framework.__ The architecture of PlatEMO is simplified, which leads to lower learning cost and higher efficiency. The result file size is also reduced.
* __Higher efficiency.__ The runtime of Pareto dominance based algorithms is reduced by using a more efficient non-dominated sorting algorithm. The runtime of decomposition based algorithms is reduced due to the new architecture of PlatEMO. The runtime of hypervolume calculation is reduced by new logic and GPU acceleration. In experimental module, the algorithms can be executed in parallel.
* __More conveniences.__ The populations obtained during the evolutionary process can be saved in result files. The references of each algorithm, problem, operator, and metric are given in the comments of the function. The codes of GUI are now open source.
# Features of PlatEMO
* Totally Developed in MATLAB
PlatEMO consists of a number of MATLAB functions without using any other libraries. Any machines able to run MATLAB can use PlatEMO regardless of the operating system.
* Includes Many Popular Algorithms
PlatEMO includes more than ninety existing popular MOEAs, including genetic algorithm, differential evolution, particle swarm optimization, memetic algorithm, estimation of distribution algorithm, and surrogate model based algorithm. Most of them are representative algorithms published in top journals after 2010.
* Various Figure Demonstrations
Users can select various figures to be displayed, including the Pareto front of the result, the Pareto set of the result, the true Pareto front, and the evolutionary trajectories of any performance indicator values.
* Powerful and Friendly GUI
PlatEMO provides a powerful and friendly GUI, where users can configure all the settings and perform experiments in parallel via the GUI without writing any code.
* Generates Data in the Format of Excel or LaTeX
Users can save the statistical experimental results generated by PlatEMO as an Excel table or LaTeX table, which can be directly used in academic writings.
# Support
* [**recommend**] You can ask any question in [issues block](https://github.com/BIMK/PlatEMO/issues) and upload your contribution by pulling request(PR).
* If you want to add your MOEA, MOP, operator or performance indicator to PlatEMO, please send the MATLAB code (able to be used in PlatEMO) and the relevant literature to field910921@gmail.com.
* If you have any question, comment or suggestion to PlatEMO or the algorithms in PlatEMO, please contact Ye Tian (field910921@gmail.com) or join the group of QQ(Group number: 100065008).
<img src="./Doc/QQgroupNumber.jpg" width=180>
# Acknowledge
This repo belongs to BIMK group and has been transferred project from [BIMK](http://bimk.ahu.edu.cn/) to github by Ye Tian and Shichen Peng[@anonymone](https://github.com/anonymone).
没有合适的资源?快使用搜索试试~ 我知道了~
温馨提示
platEMO是Ye Tian等学者写的一款基于MATLAB的多目标优化工具,为新入门的同学或者开发者提供了很大的便利,其今年已经发布到PlatEMO 2.0版本了。这款工具主要具有以下的几个特点: 1.完全由MATLAB开发,不需要任何其它库。 2.包括现有的90个流行的MOEAs,包括遗传算法、差分进化、粒子群优化、模因算法、分布估计算法和基于代理模型的算法。其中大多数是2010年以后在顶级期刊上发表的代表性算法。 3.用户可以显示各种图形,包括结果的pareto front,真实的pareto front等等。 4.强大友好的GUI,可以不用编辑任何代码。 5.可以直接生成Excel或者LaTex。
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PlatEMO-master.zip_algorithm_cakeh65_pareto_真实pareto front_遗传算法 (631个子文件)
Copyright 2KB
data 68KB
.DS_Store 6KB
.gitignore 57B
QQgroupNumber.jpg 139KB
module_experiment.m 46KB
module_test.m 21KB
GLOBAL.m 18KB
newGUI.m 12KB
WOFSMPSO.m 10KB
dacefit.m 9KB
dacefit.m 9KB
dacefit.m 9KB
dacefit.m 9KB
dacefit.m 9KB
gp.m 9KB
ParameterList.m 8KB
newPopmenu2.m 8KB
newPopmenu.m 8KB
MaOEAIT.m 7KB
GUI.m 7KB
Assignmentoptimal.m 7KB
newFigure.m 7KB
ParameterList_Item.m 7KB
Sparse_NN.m 7KB
S3CMAES.m 7KB
NDSort.m 6KB
INDIVIDUAL.m 6KB
newButtonSpecial.m 6KB
WOF_WeightIndividual.m 6KB
GA.m 6KB
DMOEAeC.m 5KB
MLDMP.m 5KB
EGOSelect.m 5KB
WFG2.m 5KB
newButtonT.m 5KB
newButton.m 5KB
WFG9.m 5KB
CMAES.m 5KB
GroupDV.m 4KB
MaF9.m 4KB
WOF_selectxPrimes.m 4KB
MOEADD.m 4KB
MaF11.m 4KB
HV.m 4KB
MaF12.m 4KB
predictor.m 4KB
predictor.m 4KB
predictor.m 4KB
predictor.m 4KB
predictor.m 4KB
newAxes.m 4KB
WOF_SMPSO_operator.m 4KB
WFG1.m 4KB
WOF_optimiseBySMPSO.m 4KB
Sparse_FS.m 4KB
UpdateArchive.m 4KB
LSMOP9.m 4KB
hpaNDSolutionsSelectionStrategy.m 4KB
UpdateFront.m 4KB
UpdateFront.m 4KB
UpdateFront.m 4KB
Sparse_PM.m 4KB
newMenu.m 4KB
MaF10.m 4KB
ENSMOEAD.m 4KB
WFG3.m 4KB
EnvironmentalSelection.m 4KB
MOEADAWA.m 4KB
module_experiment_result.m 4KB
MOEADFRRMAB.m 4KB
GAhalf.m 4KB
LMEA.m 4KB
newPlay.m 4KB
WFG7.m 4KB
WFG6.m 4KB
MOEADPaS.m 4KB
WFG8.m 4KB
GenerateBigPopulation.m 4KB
WeightOptimization.m 4KB
LocalPCA.m 4KB
WOF_createGroups.m 4KB
Contribution.m 3KB
KRVEA.m 3KB
LSMOP7.m 3KB
likGauss.m 3KB
MOEADCMA.m 3KB
LSMOP6.m 3KB
LSMOP8.m 3KB
EnvironmentalSelection.m 3KB
LSMOP4.m 3KB
newLabelButton.m 3KB
LSMOP3.m 3KB
newButtonC.m 3KB
MTS.m 3KB
SMEA.m 3KB
LSMOP2.m 3KB
DividingDistanceVariables.m 3KB
mQAP.m 3KB
newSlider.m 3KB
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