粒子群.zip_粒子群_粒子群 收敛_粒子群算法 PSO

wmax=0.9; wmin=0.4; N = 40 ; %微粒数目 D=10; a=-1; b=1; c1=2; %学习因子1 c2=2; %学习因子2 M=200 ; %最大迭代次数 xmax=100;xmin=-100; vmax=100;vmin=-100; format long; for i=1:N for j=1:D x(i,j)=randn*2; v(i,j)=randn*2; end fitness(i)=func1(x(i,:)); end [bestfitness bestindex]=min(fitness); tbest=x(bestindex,:); gbest=x; fitnessgbest=fitness; fitnesstbest=bestfitness; for i=1:M % w=((M-i)/M)*(wmax-wmin)+wmin; % w=(wmax-wmin)*tan(0.875*(1-(i/M)^0.5))+wmin; w=(wmax-wmin)*atan(1.56*(1-(i/M)^0.4))+wmin; for j=1:N v(j,:)=w*v(j,:)+c1*rand*(gbest(j,:)-x(j,:))+c2*rand*(tbest-x(j,:)); v(j,find(v(j,:)>vmax))=vmax; v(j,find(v(j,:)<vmin))=vmin; x(j,:)=x(j,:)+v(j,:); x(j,find(x(j,:)>xmax))=xmax; x(j,find(x(j,:)<xmin))=xmin; fitness(j)=func1(x(j,:)); end for j=1:N if fitness(j)<fitnessgbest(j) gbest(j,:)=x(j,:); fitnessgbest(j)=fitness(j); end if fitness(j)<fitnesstbest tbest=x(j,:); fitnesstbest=fitness(j); end end yy(i)=fitnesstbest; % yliw(i)=log10(fitnesstbest); % ytan(i)=log10(fitnesstbest); % yarc(i)=log10(fitnesstbest); end figure(2) plot(yy,'--g') hold on plot(trace,'--r') legend('PSO适应度值','DE适应度值') ylabel('适应度') xlabel('迭代的次数')