模拟退火算法优化粒子群算法求解目标函数最小值，模拟退火算法优化粒子群算法求解测试函数

clc clear close all %初始化粒子群算法参数 popsize = 300; m = 2; gen=20; %设置进化代数 xmax = [5.12 5.12]; xmin = [-5.12 -5.12]; vmax = 0.2*xmax; vmin = -vmax; fun = @shubert; pop1 = zeros(popsize,m); pop2 = zeros(popsize,m); pop3 = zeros(popsize,m); pop6 = zeros(gen,m);%存储解码后的每代最优粒子 pop7 = zeros(popsize,m);%存储更新解码后的粒子的位置 for ii1=1:popsize pop1(ii1,:)=funx(xmin,xmax,m); %初始化种群中的粒子位置， pop3(ii1,:)=pop1(ii1,:); %初始状态下个体最优值等于初始位置 pop2(ii1,:)=funv(vmax,m); %初始化种群微粒速度， pop4(ii1,1)=inf; pop5(ii1,1)=inf; end gbest_x=pop1(end,:); pop0=pop1; %设置参数 wstart = 1.0; wend = 0.9; Tmax = 6000; Vmax = 6; Vmin = -6; c1 = 1.49445; c2 = 1.49445; decayscale = 0.7; temperature = 0.00000001; markovlength = 30; k = 0; b = 0; %循环开始 for jj = 1:20 for i = 1:markovlength k = k + 1; w = wstart - (wstart - wend)*(Tmax - k)/Tmax; %循环开始 [pop1,pop2,pop3,gbest_x,pop4,pop5,b] = iterateSAPSO(pop1,pop2,pop3,gbest_x,pop4,pop5,w,c1,c2,temperature,popsize,m,b); yy(k) = pop5; end zy(jj) = min(yy); temperature = temperature*decayscale; end i = 1:6000; figure plot(zy) xlabel('迭代次数') ylabel('适应度') title('模拟退火算法优化粒子群算法') x = -2:0.1:2; y = -2:0.1:2; x = -10:0.1:10; y = -10:0.1:10; [x,y] = meshgrid(x,y); [m,n] = size(x); z = zeros(m,n); for ii = 1:m for jj = 1:n xx = [x(ii,jj) y(ii,jj)]; z(ii,jj) = shubertfun(xx); end end figure surf(x,y,z) hold on xlabel('x1') ylabel('x2') zlabel('z') axis([-2 2 -2 2 -200 200]) % axis([-10 10 -10 10 -200 200]) % shading interp title('Shubert Function') set(gca,'fontsize',12) colormap jet plot(pop0(:,1),pop0(:,2),'ro','MarkerFaceColor','r') xlabel('X') ylabel('Y') title('初始种群') set(gca,'fontsize',12) view([-130 40]) figure surf(x,y,z) hold on xlabel('x1') ylabel('x2') zlabel('z') axis([-2 2 -2 2 -200 200]) % axis([-10 10 -10 10 -200 200]) % shading interp title('Shubert Function') set(gca,'fontsize',12) colormap jet plot(pop1(:,1),pop1(:,2),'ro','MarkerFaceColor','r') xlabel('X') ylabel('Y') title('收敛后的种群') set(gca,'fontsize',12) view([-130 40]) %粒子的更新