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PlatEMO-master

于 2020-08-18 发布 文件大小:11388KB
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  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。(PlaEMO is a multi-objective optimization tool based on MATLAB written by Ye Tian and other scholars. It is a great convenience for beginners or developers. It has been released to PlatEMO version 2.0 this year. This tool has the following characteristics: 1. Developed entirely by MATLAB without any other libraries. 2. Including 90 popular MOEAs, including genetic algorithm, differential evolution, particle swarm optimization, meme algorithm, distribution estimation algorithm and proxy model-based algorithm. Most of them are representative algorithms published in top journals after 2010. 3. Users can display various graphics, including Pareto front, real Pareto front and so on. 4. Powerful and friendly GUI, you can edit no code. 5. Excel or LaTex can be generated directly.)

文件列表：

PlatEMO-master, 0 , 2019-06-27
PlatEMO-master\.github, 0 , 2019-06-27
PlatEMO-master\.github\ISSUE_TEMPLATE, 0 , 2019-06-27
PlatEMO-master\.github\ISSUE_TEMPLATE\bug_report.md, 799 , 2019-06-27
PlatEMO-master\.github\ISSUE_TEMPLATE\feature_request.md, 560 , 2019-06-27
PlatEMO-master\.gitignore, 57 , 2019-06-27
PlatEMO-master\Doc, 0 , 2019-06-27
PlatEMO-master\Doc\QQgroupNumber.jpg, 142233 , 2019-06-27
PlatEMO-master\Doc\logo.png, 51921 , 2019-06-27
PlatEMO-master\PlatEMO, 0 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms, 0 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\A-NSGA-III, 0 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\A-NSGA-III\ANSGAIII.m, 1872 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\A-NSGA-III\Adaptive.m, 1824 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\A-NSGA-III\EnvironmentalSelection.m, 3192 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\AGE-II, 0 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\AGE-II\AGEII.m, 1587 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\AGE-II\CrowdingDistance.m, 1353 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\AGE-II\EnvironmentalSelection.m, 2128 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\AGE-II\MatingSelection.m, 1033 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\AGE-II\UpdateArchive.m, 763 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\AR-MOEA, 0 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\AR-MOEA\ARMOEA.m, 1668 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\AR-MOEA\CalDistance.m, 1345 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\AR-MOEA\EnvironmentalSelection.m, 3017 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\AR-MOEA\MatingSelection.m, 2198 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\AR-MOEA\UpdateRefPoint.m, 2552 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\BCE-IBEA, 0 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\BCE-IBEA\BCEIBEA.m, 1717 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\BCE-IBEA\CalFitness.m, 1001 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\BCE-IBEA\EnvironmentalSelection.m, 979 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\BCE-IBEA\Exploration.m, 1472 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\BCE-IBEA\PCSelection.m, 1465 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\BCE-MOEA-D, 0 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\BCE-MOEA-D\BCEMOEAD.m, 2763 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\BCE-MOEA-D\Exploration.m, 1472 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\BCE-MOEA-D\PCSelection.m, 1465 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\BiGE, 0 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\BiGE\BiGE.m, 1229 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\BiGE\EnvironmentalSelection.m, 1409 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\BiGE\Estimation.m, 1265 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\BiGE\MatingSelection.m, 983 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\C-MOEA-D, 0 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\C-MOEA-D\CMOEAD.m, 2691 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\CA-MOEA, 0 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\CA-MOEA\CAMOEA.m, 2481 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\CA-MOEA\Reference_Generation.m, 1983 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\CA-MOEA\Reference_Point_Selection.m, 2921 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\CMOPSO, 0 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\CMOPSO\CMOPSO.m, 1248 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\CMOPSO\CrowdingDistance.m, 1353 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\CMOPSO\EnvironmentalSelection.m, 1731 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\CMOPSO\Operator.m, 2579 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\CPS-MOEA, 0 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\CPS-MOEA\CPSMOEA.m, 1659 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\CPS-MOEA\KNN.m, 1027 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\CPS-MOEA\NDS.m, 1709 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\CPS-MOEA\Operator.m, 1140 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\CSEA, 0 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\CSEA\CSEA.m, 2582 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\CSEA\DataProcess.m, 1279 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\CSEA\FNN.m, 3176 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\CSEA\GetOutput.m, 881 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\CSEA\RadarGrid.m, 1548 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\CSEA\RefSelect.m, 2569 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\CSEA\SurrogateAssistedSelection.m, 1630 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\CSEA\lhsamp.m, 592 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\DMOEA-eC, 0 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\DMOEA-eC\DMOEAeC.m, 5235 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\DMOEA-eC\UpdateArchive.m, 1609 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\EAG-MOEA-D, 0 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\EAG-MOEA-D\EAGMOEAD.m, 2117 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\EAG-MOEA-D\MatingSelection.m, 1070 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\EAG-MOEA-D\UpdateArchive.m, 1701 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\EAG-MOEA-D\UpdatePopulation.m, 1015 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\EFR-RR, 0 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\EFR-RR\EFRRR.m, 1673 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\EFR-RR\EnvironmentalSelection.m, 1203 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\EFR-RR\MaximumRanking.m, 1308 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\EFR-RR\Normalization.m, 1419 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\EIM-EGO, 0 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\EIM-EGO\DACE, 0 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\EIM-EGO\DACE\corrgauss.m, 1028 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\EIM-EGO\DACE\dacefit.m, 9151 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\EIM-EGO\DACE\lhsamp.m, 592 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\EIM-EGO\DACE\predictor.m, 4398 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\EIM-EGO\DACE\regpoly0.m, 377 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\EIM-EGO\DACE\regpoly1.m, 385 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\EIM-EGO\EIMEGO.m, 2567 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\EIM-EGO\InfillSamplingEIM.m, 3172 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\EMyO-C, 0 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\EMyO-C\Clustering.m, 2061 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\EMyO-C\EMyOC.m, 1558 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\EMyO-C\EnvironmentalSelection.m, 1133 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\EMyO-C\Operator.m, 1819 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\EMyO-C\Truncation.m, 1430 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\ENS-MOEA-D, 0 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\ENS-MOEA-D\ENSMOEAD.m, 3986 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\GDE3, 0 , 2019-06-27
PlatEMO-master\PlatEMO\Algorithms\GDE3\EnvironmentalSelection.m, 2764 , 2019-06-27

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    可以得到很精确的幅值、频率、相位估计，相控阵天线的方向图（切比雪夫加权），用于信号特征提取、信号消噪，数据模型归一化，模态振动，包括最小二乘法、SVM、神经网络、1_k近邻法,IDW距离反比加权方法。( You can get a very accurate amplitude, frequency, phase estimation, Phased array antenna pattern (Chebyshev weights), For feature extraction, signal de-noising, Normalized data model, modal vibration, Including the least squares method, the SVM, neural networks, 1 _k neighbor method, IDW inverse distance weighting method.)
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