opl.rar_opl

clc clear close all format shortG %% parameters setting data=load('data.mat'); nvar=data.Nj+data.Nm-1; popsize=50; maxiter=100; pc=0.5; ncross=2*round((popsize*pc)/2); pm=1-pc; nmut=round(popsize*pm); %% initial population algorithm tic emp.x=[]; emp.info=[]; emp.fit=[]; pop=repmat(emp,popsize,1); for i=1:popsize pop(i).x=randperm(nvar); pop(i)=fitness(pop(i),data); end [value,index]=min([pop.fit]); gpop=pop(index); %% main loop algorithm BEST=zeros(maxiter,1); MEAN=zeros(maxiter,1); for iter=1:maxiter % crossover crosspop=repmat(emp,ncross,1); crosspop=crossover(crosspop,pop,nvar,data,ncross); % mutation mutpop=repmat(emp,nmut,1); mutpop=mutation(mutpop,pop,nvar,data,nmut,popsize); [pop]=[pop;crosspop;mutpop]; [value,index]=sort([pop.fit]); pop=pop(index); gpop=pop(1); pop=pop(1:popsize); BEST(iter)=gpop.fit; MEAN(iter)=mean([pop.fit]); disp([' Iter = ' num2str(iter) ' BEST = ' num2str(BEST(iter))]) end %% results algorithm disp('=========================================================') disp([ ' Best Solution = ' num2str(gpop.x)]) disp([ ' Best Fitness = ' num2str(gpop.fit)]) disp([ ' Time = ' num2str(toc)]) disp('=========================================================') for m=1:data.Nm disp([ ' Machine = ' num2str(m) ' Time = ' num2str(gpop.info.MachineTime(m)) ' Job = ' num2str(gpop.info.Machine{m})]) end disp('=========================================================') figure(1) plot(BEST,'r') hold on plot(MEAN,'b') xlabel('Iteration') ylabel('Fitness') legend('BEST','MEAN') title('GA-PMP')