matlab基本粒子群算法实现（四）

%% 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) = fun(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) = fun(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);

function y = fun(x)
%函数用于计算粒子适应度值
%x           input           输入粒子 
%y           output          粒子适应度值 
y = x(1).^2 + x(2).^2 - 10*cos(2*pi*x(1)) - 10*cos(2*pi*x(2)) + 20;