多目标遗传算法代码.doc

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1、.%functionnsga_2(pro)%%MainFunction%MainprogramtoruntheNSGA-IIMOEA.%Readthecorrespondingdocumentationtolearnmoreaboutmultiobjective%optimizationusingevolutionaryalgorithms.%initialize_variableshastwoarguments;Firstbeingthepopulationsize%andthesecondtheproblemnumber.'1'correspondstoMOP1and'2'%c

2、orrespondstoMOP2.%inp_para_definition=input_parameters_definition;%%Initializethevariables%Declarethevariablesandinitializetheirvalues%pop-population%gen-generations%pro-problemnumber%clear;clc;tic;pop=100;%每一代的种群数gen=100;%总共的代数pro=2;%问题选择1或者2，见switchswitchprocase1%Misthenumberofobjectives.M=2

3、;%Visthenumberofdecisionvariables.Inthiscaseitis%difficulttovisualizethedecisionvariablesspacewhilethe%objectivespaceisjusttwodimensional.V=6;case2M=3;V=12;case3%case1和case2用来对整个算法进行常规验证，作为调试之用；case3为本工程所需；M=2;%(outputparameters个数)V=8;%(inputparameters个数)K=10;end%Initializethepopulationchromos

4、ome=initialize_variables(pop,pro);%%Sorttheinitializedpopulation%Sortthepopulationusingnon-domination-sort.Thisreturnstwocolumns%foreachindividualwhicharetherankandthecrowdingdistance%correspondingtotheirpositioninthefronttheybelong.真是牛X了。chromosome=non_domination_sort_mod(chromosome,pro);%%St

5、arttheevolutionprocess..%Thefollowingareperformedineachgeneration%Selecttheparents%PerfromcrossoverandMutationoperator%PerformSelectionfori=1:gen%Selecttheparents%Parentsareselectedforreproductiontogenerateoffspring.The%originalNSGA-IIusesabinarytournamentselectionbasedonthe%crowded-comparisio

6、noperator.Theargumentsare%pool-sizeofthematingpool.Itiscommontohavethistobehalfthe%populationsize.%tour-Tournamentsize.OriginalNSGA-IIusesabinarytournament%selection,buttoseetheeffectoftournamentsizethisiskept%arbitary,tobechoosenbytheuser.pool=round(pop/2);tour=2;%下面进行二人锦标赛配对，新的群体规模是原来群体的一半pa

7、rent_chromosome=tournament_selection(chromosome,pool,tour);%PerfromcrossoverandMutationoperator%TheoriginalNSGA-IIalgorithmusesSimulatedBinaryCrossover(SBX)and%Polynomialcrossover.Crossoverprobabilitypc=0.9andmutation%probabilityispm=1/