simuapso.zip_simuapso_智能算法_模拟退火 粒子群

function[xm,fv]=SimuAPSO(fitness,N,c1,c2,lamda,M,D) format long; for i=1:N for j=1:D x(i,j)=randn; v(i,j)=randn; end end for i=1:N p(i)=fitness(x(i,:)); y(i,:)=x(i,:); end pg=x(N,:); for i=1:(N-1) if fitness(x(i,:))<fitness(pg) pg=x(i,:); end end T=fitness(pg)/log(5); for t=1:M groupFit=fitness(pg); for i=1:N Tfit(i)=exp(-(p(i)-groupFit)/T); end SumTfit=sum(Tfit); Tfit=Tfit/SumTfit; pBet=rand(); for i=1:N ComFit(i)=sum(Tfit(1:i)); if pBet<=ComFit(i) pg_plus=x(i,:); break; end end C=c1+c2; ksi=2/abs(2-C-sqrt(C^2-4*C)); for i=1:N v(i,:)=ksi*(v(i,:)+c1*rand*(y(i,:)-x(i,:))+c2*rand*(pg_plus-x(i,:))); x(i,:)=x(i,:)+v(i,:); if fitness(x(i,:))<p(i) p(i)=fitness(x(i,:)); y(i,:)=x(i,:); end if p(i)<fitness(pg) pg=y(i,:); end end T=T*lamda; Pbest(t)=fitness(pg); end xm=pg'; fv=fitness(pg); %-------输出每一步的进化迭代曲线 %plot(Pbest,'r-o') %hold on %axis([0,500,0.4,0.5]) %title(['适应度曲线 ' '终止代数＝' num2str(M)]); %xlabel('进化代数');ylabel('适应度');