GAPSO.zip_GAPSO_metahuristic

clc clear close all format shortG %% parameters setting nvar=10; lb.var=-10*ones(1,nvar); ub.var= 10*ones(1,nvar); lb.v=-0.8; ub.v=0.8; popsize=100; maxiter=350; pc=0.9; ncross=2*round((popsize*pc)/2); pm=1-pc; nmut=round(popsize*pm); W=1; C1=2; C2=2; W_RF=0.97; %% initial population algorithm tic emp.var=[]; emp.fit=[]; emp.v=[]; pop=repmat(emp,popsize,1); for i=1:popsize pop(i).var=lb.var+rand(1,nvar).*(ub.var-lb.var); pop(i).fit=fitness(pop(i).var); pop(i).v=0; end [value,index]=min([pop.fit]); gpop=pop(index); bpop=pop; %% main loop algorithm BEST=zeros(maxiter,1); for iter=1:maxiter % PSO PSOpop=PSOfunc(pop,bpop,W,C1,C2,nvar,lb,ub); % crossover crosspop=repmat(emp,ncross,1); crosspop=crossover(crosspop,pop,ncross); % mutation mutpop=repmat(emp,nmut,1); mutpop=mutation(mutpop,pop,nvar,nmut,popsize,lb,ub); [pop]=[pop;PSOpop;crosspop;mutpop]; [value,index]=sort([pop.fit]); pop=pop(index); gpop=pop(1); pop=pop(1:popsize); BEST(iter)=gpop.fit; disp([' Iter = ' num2str(iter) ' BEST = ' num2str(BEST(iter))]) W=W*W_RF; end %% results algorithm disp([ ' Best Solution = ' num2str(gpop.var)]) disp([ ' Best Fitness = ' num2str(gpop.fit)]) disp([ ' Time = ' num2str(toc)]) figure(1) plot(BEST,'r') xlabel('Iteration') ylabel('Fitness') legend('BEST') title('GAPSO') %% Matlabhome.ir *** Shuffled frog leaping algorithm %% Free Download *** Farsi Videos & Matlab Code &...