粒子群算法优化的matlab程序

function [xm,fv] = CLSPSO(fitness,N,c1,c2,w,xmax,xmin,M,MaxC,D) format long; %------初始化种群的个体------------ for i=1:N for j=1:D x(i,j)=randn; %随机初始化位置 v(i,j)=randn; %随机初始化速度 end end %------先计算各个粒子的适应度，并初始化Pi和Pg---------------------- for i=1:N p(i)=fitness(x(i,:)); y(i,:)=x(i,:); end pg = x(N,:); %Pg为全局最优 for i=1:(N-1) if fitness(x(i,:))<fitness(pg) pg=x(i,:); end end %------进入主要循环，按照公式依次迭代------------ for t=1:M for i=1:N v(i,:)=w*v(i,:)+c1*rand*(y(i,:)-x(i,:))+c2*rand*(pg-x(i,:)); x(i,:)=x(i,:)+v(i,:); fv(i) = fitness(x(i,:)); end [sort_fv,index] = sort(fv); Nbest = floor(N*0.2); for n=1:Nbest tmpx = x(index(n),:); for k=1:MaxC for dim=1:D cx(dim) = (tmpx(1,dim) - xmin(dim))/(tmpx(1,dim) - xmax(dim)); cx(dim) = 4*cx(dim)*(1 - cx(dim)); tmpx(1,dim) = tmpx(1,dim) + cx(dim)*(xmax(dim) - xmin(dim)); end fcs = fitness(tmpx); if fcs < sort_fv(n) x(index(n),:) = tmpx; break; end end x(index(n),:) = tmpx; end r = rand(); for s=1:D xmin(s) = max(xmin(s) , pg(s) - r*(xmax(s) - xmin(s))); xmax(s) = min(xmax(s) , pg(s) + r*(xmax(s) - xmin(s))); end x(1:Nbest, :) = x(index(1:Nbest),:); for i=(Nbest+1):N for j=1:D x(i,j)= xmin(j) + rand*(xmax(j) - xmin(j)); %随机初始化位置 v(i,j)= randn; %随机初始化速度 end end Pbest(t)=fitness(pg); for i=1:N 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 end xm = pg'; fv = fitness(pg);