function[xm,fv]=SimuAPSO(fitness,N,c1,c2,lamda,M,D)
%格式
format long;
%初始化种群的个体
for i=1:N
for j=1:D
x(i,j)=randn;
v(i,j)=randn;
end
end
%先计算适应度，并初始化pi和pg
for i=1:N
p(i)=fitness(x(i,:));
y(i,:)=x(i,:);
end
pg=x(N,:);%全局最优
for i=1:(N-1)
if fitness(x(i,:))<fitness(pg)
pg=x(i,:);
end
end
%进入主循环，按公式迭代
T=-fitness(pg)/log(0.2);
for t=1:M
groupFit=fitness(pg);
for i=1:N
Tfit(i)=exp(-(p(i)-groupFit)/T);
end
SumTfit=sum(Tfit);
Tfit=Tfit/SumTfit;
pBet=rand();
for i=1:N
ComFit(i)=sum(Tfit(1:i));
if pBet<=ComFit(i)
pg_plus=x(i,:);
break;
end
end
C=c1+c2;
ksi=2/abs(2-C-sqrt(C^2-4*C));
for i=1:N
v(i,:)=ksi*(v(i,:)+c1*rand*(y(i,:)-x(i,:))+c2*rand*(pg_plus-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
T=T*lamda;
Pbest(t)=fitness(pg);
end
r=[1:1:30];
plot(r,Pbest,'r--','linewidth',2);
xlabel('迭代次数')
ylabel('适应度值')
title('改进算法收敛曲线')
legend('基于模拟退火的pso算法')
grid on
hold on
xm=pg';
fv=fitness(pg);
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