%% I. 清空环境
clc
clear
%% II. 绘制目标函数曲线
figure
[x,y] = meshgrid(-5:0.1:5,-5:0.1:5);
z = x.^2 + y.^2 - 10*cos(2*pi*x) - 10*cos(2*pi*y) + 20;
mesh(x,y,z)
hold on
%% III. 参数初始化
c1 = 1.49445;
c2 = 1.49445;
maxgen = 1000; % 进化次数
sizepop = 100; %种群规模
Vmax = 1;
Vmin = -1;
popmax = 5;
popmin = -5;
%% IV. 产生初始粒子和速度
for i = 1:sizepop
% 随机产生一个种群
pop(i,:) = 5*rands(1,2); %初始种群
V(i,:) = rands(1,2); %初始化速度
% 计算适应度
fitness(i) = fun2(pop(i,:)); %染色体的适应度
end
%% V. 个体极值和群体极值
[bestfitness bestindex] = max(fitness);
zbest = pop(bestindex,:); %全局最佳
gbest = pop; %个体最佳
fitnessgbest = fitness; %个体最佳适应度值
fitnesszbest = bestfitness; %全局最佳适应度值
%% VI. 迭代寻优
for i = 1:maxgen
for j = 1:sizepop
% 速度更新
V(j,:) = V(j,:) + c1*rand*(gbest(j,:) - pop(j,:)) + c2*rand*(zbest - pop(j,:));
V(j,find(V(j,:)>Vmax)) = Vmax;
V(j,find(V(j,:)<Vmin)) = Vmin;
% 种群更新
pop(j,:) = pop(j,:) + V(j,:);
pop(j,find(pop(j,:)>popmax)) = popmax;
pop(j,find(pop(j,:)<popmin)) = popmin;
% 适应度值更新
fitness(j) = fun2(pop(j,:));
end
for j = 1:sizepop
% 个体最优更新
if fitness(j) > fitnessgbest(j)
gbest(j,:) = pop(j,:);
fitnessgbest(j) = fitness(j);
end
% 群体最优更新
if fitness(j) > fitnesszbest
zbest = pop(j,:);
fitnesszbest = fitness(j);
end
end
yy(i) = fitnesszbest;
end
%% VII.输出结果
[fitnesszbest, zbest]
plot3(zbest(1), zbest(2), fitnesszbest,'bo','linewidth',1.5)
figure
plot(yy)
title('最优个体适应度','fontsize',12);
xlabel('进化代数','fontsize',12);ylabel('适应度','fontsize',12);
