GA-TSP.rar_algorithms

clc clear close all %% paramters setting load data nvar=size(a,1); % number of variable [dis]=cal_dis(a); npop=100; % number of population maxiter=550; % max of iteration pc=0.1; % percent of crossover ncross=2*round(npop*pc/2); % number of cross over offspring pm=1-pc; % percent of mutation nmut=round(npop*pm); % number of mutation offsprig %% initialization tic empty.par=[]; empty.fit=[]; pop=repmat(empty,npop,1); for i=1:npop pop(i).par=randperm(nvar); pop(i).fit=fitness(pop(i).par,dis,nvar); end %% main loop BEST=zeros(maxiter,1); MEAN=zeros(maxiter,1); for iter=1:maxiter % crossover crosspop=repmat(empty,ncross,1); crosspop=crossovert(crosspop,pop,nvar,ncross,dis); % mutation mutpop=repmat(empty,nmut,1); mutpop=mutation(mutpop,pop,nvar,dis,nmut,npop); % merged [pop]=[pop;crosspop;mutpop]; % select [value,index]=sort([pop.fit]); pop=pop(index); pop=pop(1:npop); gpop=pop(1); % global pop BEST(iter)=gpop.fit; MEAN(iter)=mean([pop.fit]); disp([ ' Iter = ' num2str(iter) ' BEST = ' num2str(BEST(iter))]); if iter>150 && BEST(iter)==BEST(iter-150) break end end %% results disp(' ') disp([ ' Best par = ' num2str(gpop.par)]) disp([ ' Best fitness = ' num2str(gpop.fit)]) disp([ ' Time = ' num2str(toc)]) figure(1) plot(BEST(1:iter),'r','LineWidth',2) hold on plot(MEAN(1:iter),'b','LineWidth',2) xlabel('Iteration') ylabel(' Fitness') legend('BEST','MEAN') title('GA for TSP') %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % % www.matlabnet.ir % % Free Download matlab code and movie % % Shahab Poursafary % % % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%