function [gbest,g,Convergence_curve]=PSO(N,T,lb,ub,dim,fobj)
%% 定义粒子群算法参数
% N 种群 T 迭代次数
%% 随机初始化种群
D=dim; %粒子维数
c1=1.5; %学习因子1
c2=1.5; %学习因子2
w=0.8; %惯性权重
Xmax=ub; %位置最大值
Xmin=lb; %位置最小值
Vmax=ub; %速度最大值
Vmin=lb; %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)=fobj(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
%%%%%%%%%%%按照公式依次迭代直到满足精度或者迭代次数%%%%%%%%%%%%%
for i=1:T
i
for j=1:N
%%%%%%%%%%%%%%更新个体最优位置和最优值%%%%%%%%%%%%%%%%%
if (fobj(x(j,:))) <pbest(j)
p(j,:)=x(j,:);
pbest(j)=fobj(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
%%%%%%%%%%%%%%%%%跟新位置和速度值%%%%%%%%%%%%%%%%%%%%%
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%%%%%%%%%%%%%%%%%%%%%%
if length(Vmax)==1
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
else
for ii=1:D
if (v(j,ii)>Vmax(ii)) | (v(j,ii)< Vmin(ii))
v(j,ii)=rand * (Vmax(ii)-Vmin(ii))+Vmin(ii);
end
if (x(j,ii)>Xmax(ii)) | (x(j,ii)< Xmin(ii))
x(j,ii)=rand * (Xmax(ii)-Xmin(ii))+Xmin(ii);
end
end
end
end
%%%%%%%%%%%%%%%%%%%%记录历代全局最优值%%%%%%%%%%%%%%%%%%%%%
Convergence_curve(i)=gbest;%记录训练集的适应度值
end
