function [xm,fv] = PSO(fitness,N,c1,c2,w,M,D)
format long;
%------初始化种群的个体------------
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
%------先计算各个粒子的适应度，并初始化Pi和Pg----------------------
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
%------进入主要循环，按照公式依次迭代------------
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);
