aco.zip_A Family To Share_ant colony_metaheuristic_optimization_

clc clear all close all format shortG %% parameters setting L=xlsread('data.xlsx'); % Location matrix nvar=size(L,1); % number of variable dis=calculated_distance(L,nvar); % distance matrix n_ant=100; % number of ant maxiter=400; % max of iteration %% initialization tau0=1; tau=tau0*ones(nvar,nvar); % phromone matrix tau=tau-diag(diag(tau)); etha=1./dis; % hueristic information matrix etha=etha-diag(diag(etha)); Q=100; alpha=2; beta=1; Roh=0.02; %% main loop tic empty.pos=[]; empty.cost=[]; ant=repmat(empty,n_ant,1); % population gant.pos=[]; % global ant gant.cost=inf; best=zeros(maxiter,1); AVR=zeros(maxiter,1); for iter=1:maxiter for i=1:n_ant tour=randi([1 nvar]); for j=2:nvar P=(tau(tour(end),:).^alpha).*(etha(tour(end),:).^beta); P(tour)=0; k=RouletteWheel(P); [tour]=[tour k]; end ant(i).pos=tour; ant(i).cost=fitness(tour,nvar,dis); end [value,index]=min([ant.cost]); if value<gant.cost gant=ant(index); end for i=1:n_ant tour=ant(i).pos; L=ant(i).cost; [tour]=[tour tour(1)]; for j=1:nvar tau(tour(j),tour(j+1))=tau(tour(j),tour(j+1))+(Q/L); end end tau=tau*(1-Roh); best(iter)=gant.cost; AVR(iter)=mean([ant.cost]); disp([ ' iter = ' num2str(iter) ' BEST = ' num2str(best(iter)) ]) if iter>60 && best(iter)==best(iter-60) && mod(iter,30)==0 disp( ' RESTART ') tau0=1; tau=tau0*ones(nvar,nvar); % phromone matrix tau=tau-diag(diag(tau)); end if iter>150 && best(iter)==best(iter-150) break end end %% results disp('======================================================') disp([ ' best solution = ' num2str(gant.pos)]); disp([ ' best fitness = ' num2str(gant.cost)]); disp([ ' Time = ' num2str(toc)]) figure(1) plot(best(1:iter),'r','LineWidth',2); hold on plot(AVR(1:iter),'b','LineWidth',2); xlabel(' iteration ') ylabel('fitness') legend('BEST','MEAN') title(' ACO for TSP ') %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % % www.matlabnet.ir % % Free Download matlab code and movie % % Shahab Poursafary % % % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%