clc,clear,close all
warning off
format longG
% SA 参数
maxiter =1000; % 迭代次数
sizepop = 1000; % 种群数量
popmin = -50; popmax = 50; % x1
% popmin2 = -10; popmax2 = 10; % x2
T0 =8000; % initiation temperature
q = 0.9; % 降温ratio
Tend=10.^(-10); % 终止问题
% L = 100; % 链长
% 初始化种群
for i=1:sizepop
pop(i,:) = popmin + (popmax-popmin)*rand(1,3);
fitness(i) = fun( pop(i,:));
end
% 记录一组最优值
[bestfitness,bestindex]=min(fitness);
zbest=pop(bestindex,:); % 全局最佳
fitnesszbest=bestfitness; % 全局最佳适应度值
count = 1;
% 迭代寻优
% while T0>Tend % 等价于迭代次数% maxiter = 200; % 迭代次数
for i=1: maxiter
for j=1:sizepop
% 产生新解
S1 = popmin + (popmax-popmin)*rand(1,3);
% Metropolis法则判断是否接受新解
[S,fitness3] = Metropolis(S1,pop(j,:),T0);
pop(j,:) = S;
% 适应度更新
fitness(j) = fitness3;
% 比较 个体间比较
if fitness(j)<bestfitness
bestfitness = fitness(j);
zbest = pop(j,:);
end
end
% T0 = q*T0; % 更新当前温度
fitness_iter(i) = bestfitness;
% fitness_iter(count) = bestfitness;
% count = count+1;
end
disp('最优解')
disp(zbest)
fprintf('\n')
bestfitness
figure('color',[1,1,1])
% loglog(fitness_iter,'ro-','linewidth',2)
plot(fitness_iter,'b','linewidth',2)
grid on
axis tight
