PSO.m

function [xm, fv] = PSO(fitness, N, c1, c2, w, M, D)
%待优化的目标函数：fitness
%粒子数目：N
%学习因子1：c1
%学习因子2：c2
%惯性权重：w
%最大迭代次数：M
%问题的维数：D
%目标函数取最小值时的自变量值：xm
%目标函数的最小值：fv

format long;

x = zeros(N, D);
v = zeros(N, D);
y = zeros(N, D);
p = zeros(1, N);
Pbest = zeros(1, M);

for i=1:N
    for j=1:D
        x(i, j)=randn;  %随机初始化位置
        v(i, j)=randn;  %随机初始化速度
    end
end

for i=1:N
    p(i)=fitness(x(i,:));
    y(i,:)=x(i,:);
end
pg=x(N,:);  %pg为全局最优
for i=1:(N-1)
    if fitness(x(i,:))<fitness(pg)
        pg=x(i,:);
    end
end
for t=1:M
	for i=1:N
		v(i,:)=w*v(i,:)+c1*rand*(y(i,:)-x(i,:))+c2*rand*(pg-x(i,:));
		x(i,:)=x(i,:) + v(i,:);
		if fitness(x(i,:)) < p(i)
			p(i)=fitness(x(i,:));
			y(i,:)=x(i,:);
		end
		if p(i)<fitness(pg)
			pg=y(i,:);
		end
	end
	Pbest(t)=fitness(pg);
end
xm=pg';
fv=fitness(pg);