%%%%%%%%%%%%%%%%%%%%%%模拟退火算法解决函数极值%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%初始化%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clear all; %清除所有变量
close all; %清图
clc; %清屏
D=10; %变量维数
Xs=20; %上限
Xx=-20; %下限
%%%%%%%%%%%%%%%%%%%%%%%%%%%冷却表参数%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
L = 200; %马可夫链长度
K = 0.998; %衰减参数
S = 0.01; %步长因子
T=100; %初始温度
YZ = 1e-8; %容差
P = 0; %Metropolis过程中总接受点
%%%%%%%%%%%%%%%%%%%%%%%%%%随机选点 初值设定%%%%%%%%%%%%%%%%%%%%%%%%%
PreX = rand(D,1)*(Xs-Xx)+Xx;
PreBestX = PreX;
PreX = rand(D,1)*(Xs-Xx)+Xx;
BestX = PreX;
%%%%%%%%%%%每迭代一次退火一次(降温), 直到满足迭代条件为止%%%%%%%%%%%%
deta=abs( func1( BestX)-func1(PreBestX));
while (deta > YZ) && (T>0.001)
T=K*T;
%%%%%%%%%%%%%%%%%%%%%在当前温度T下迭代次数%%%%%%%%%%%%%%%%%%%%%%
for i=1:L
%%%%%%%%%%%%%%%%%在此点附近随机选下一点%%%%%%%%%%%%%%%%%%%%%
NextX = PreX + S* (rand(D,1) *(Xs-Xx)+Xx);
%%%%%%%%%%%%%%%%%边界条件处理%%%%%%%%%%%%%%%%%%%%%%%%%%
for ii=1:D
if NextX(ii)>Xs | NextX(ii)<Xx
NextX(ii)=PreX(ii) + S* (rand *(Xs-Xx)+Xx);
end
end
%%%%%%%%%%%%%%%%%%%%%%%是否全局最优解%%%%%%%%%%%%%%%%%%%%%%
if (func1(BestX) > func1(NextX))
%%%%%%%%%%%%%%%%%%保留上一个最优解%%%%%%%%%%%%%%%%%%%%%
PreBestX = BestX;
%%%%%%%%%%%%%%%%%%%此为新的最优解%%%%%%%%%%%%%%%%%%%%%%
BestX=NextX;
end
%%%%%%%%%%%%%%%%%%%%%%%% Metropolis过程%%%%%%%%%%%%%%%%%%%
if( func1(PreX) - func1(NextX) > 0 )
%%%%%%%%%%%%%%%%%%%%%%%接受新解%%%%%%%%%%%%%%%%%%%%%%%%
PreX=NextX;
P=P+1;
else
changer = -1*(func1(NextX)-func1(PreX))/ T ;
p1=exp(changer);
%%%%%%%%%%%%%%%%%%%%%%%%接受较差的解%%%%%%%%%%%%%%%%%%%%
if p1 > rand
PreX=NextX;
P=P+1;
end
end
trace(P+1)=func1( BestX);
end
deta=abs( func1( BestX)-func1 (PreBestX));
end
disp('最小值在点:');
BestX
disp( '最小值为:');
func1(BestX)
figure
plot(trace(2:end))
xlabel('迭代次数')
ylabel('目标函数值')
title('适应度进化曲线')
