NSGA-II-matlab.rar_NSGA_多目标运行_收敛性_遗传算法

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % MATLAB Code for % % % % Non-dominated Sorting Genetic Algorithm II (NSGA-II) % % Version 1.0 - April 2010 % % % % Programmed By: S. Mostapha Kalami Heris % % % % e-Mail: sm.kalami@gmail.com % % kalami@ee.kntu.ac.ir % % % % Homepage: http://www.kalami.ir % % % % nsga2.m : main file of the program % % % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% clc; clear; close all; CostFunction=@Cost; npop=50; % main population size nvar=3; % number of unknown variables nobj=numel(CostFunction(zeros(1,nvar))); % number of objective functions maxit=100; % maximum number of iterations VarMin=-5; % lower bound of unknown variables VarMax= 5; % upper bound of unknown variables VarRange=[VarMin VarMax]; % range of unknown variables pc=0.8; % crossover ratio nc=round(pc*npop/2)*2; % number of parents (also offsprings) pm=0.3; % mutation ratio nm=round(pm*npop); % number of mutants % initialization pop=CreateEmptyIndividuals(npop); for i=1:numel(pop) pop(i).Position=unifrnd(VarMin,VarMax,[1 nvar]); pop(i).Cost=CostFunction(pop(i).Position); end % main loop for it=1:maxit [pop F]=NonDominatedSorting(pop); pop=CalcCrowdingDistance(pop,F); disp(['Iteration ' num2str(it) ': Number of 1st Front Individuals = ' num2str(numel(F{1}))]); if it==maxit break; end pop2=CreateEmptyIndividuals(nc); for k=1:nc/2 p1=BinaryTournamentSelection(pop); p2=BinaryTournamentSelection(pop); ch=Crossover(p1,p2); ch(1).Cost=CostFunction(ch(1).Position); ch(2).Cost=CostFunction(ch(2).Position); pop2(2*k-1)=ch(1); pop2(2*k)=ch(2); end pop3=CreateEmptyIndividuals(nm); for k=1:nm p=BinaryTournamentSelection(pop); q=Mutate(p,VarRange); q.Cost=CostFunction(q.Position); pop3(k)=q; end pop=[pop pop2 pop3]; [pop F]=NonDominatedSorting(pop); pop=CalcCrowdingDistance(pop,F); pop=SortPopulation(pop); pop=pop(1:npop); end PlotFronts(pop,F);