%%%%%%%%%%%%% 粒子群算法求函数极值 %%%%%%%%%%%%%
clear;clc;
N = 100; % 群体例子个数
D = 2; % 粒子维度
T = 200; % 最大迭代次数
c1 = 1.5; % 学习因子1
c2 = 1.5; % 学习因子2
Wmax = 0.8; % 惯性权重最大值
Wmin = 0.4; % 惯性权重最小值
Xmax = 600; % 位置最大值
Xmin = -600; % 位置最小值
Vmax = 600; % 速度最大值
Vmin = -600; % 速度最小值
%%%%%%%%%% 初始化种群个体（限定位置和速度） %%%%%%%%%
x = rand(N, D)*(Xmax - Xmin) + Xmin;
v = rand(N, D)*(Vmax - Vmin) + Vmin;
%%%%%%%%%% 初始化个体最优位置和最优值 %%%%%%%%%
p = x;
pbest = ones(N, 1);
for i = 1:N
pbest(i) = func2(x(i, :));
end
%%%%%%%%%% 初始化全局最优位置和最优值 %%%%%%%%%
g = ones(1, D);
gbest = inf;
for i = 1:N
if pbest(i) < gbest
g = p(i, :);
gbest = pbest(i);
end
end
gb = ones(1, T);
%%%%%%%%%% 按照公式依次迭代直到满足精度或者迭代次数 %%%%%%%%%
for i = 1:T
for j = 1:N
%%%%%%%%%% 更新个体最优位置和最优值 %%%%%%%%%
if func2(x(j, :)) < pbest(j)
p(j, :) = x(j, :);
pbest(j) = func2(x(j, :));
end
%%%%%%%%%% 更新全局最优位置和最优值 %%%%%%%%%
if pbest(j) < gbest
g = p(j, :);
gbest = pbest(j);
end
%%%%%%%%%% 动态计算惯性权重值 %%%%%%%%%
w = Wmax -(Wmax - Wmin)*i/T;
%%%%%%%%%% 更新位置和速度值 %%%%%%%%%
v(j, :) = w*v(j, :) + c1*rand*(p(j, :) - x(j, :)) + c2*rand*(g - x(j, :));
x(j, :) = x(j, :) + v(j, :);
%%%%%%%%%% 边界条件处理 %%%%%%%%%
for ii = 1:D
if (v(j, ii) > Vmax) || (v(j, ii) < Vmin)
v(j, ii) = rand*(Vmax - Vmin) + Vmin;
end
if (x(j, ii) > Xmax) || (x(j, ii) < Xmin)
x(j, ii) = rand*(Xmax - Xmin) + Xmin;
end
end
end
%%%%%%%%%% 记录历代全局最优值 %%%%%%%%%
gb(i) = gbest;
end
disp(['最优个体：' num2str(g)]);
disp(['最优值：' num2str(gb(end))]);
plot(gb, 'LineWidth', 2);
xlabel('迭代次数');
ylabel('适应度值');
title('适应度进化曲线');
%%%%%%%%%% 适应度函数 %%%%%%%%%%%
