输出结果

实现代码

%SA：利用SA算法解决TSP(数据是14个虚拟城市的横纵坐标)问题——Jason niu

X = [16.4700   96.1000

    16.4700   94.4400

    20.0900   92.5400

    22.3900   93.3700

    25.2300   97.2400

    22.0000   96.0500

    20.4700   97.0200

    17.2000   96.2900

    16.3000   97.3800

    14.0500   98.1200

    16.5300   97.3800

    21.5200   95.5900

    19.4100   97.1300

    20.0900   92.5500];

D = Distance(X);  

N = size(D,1);    

T0 = 1e10;    

Tend = 1e-30;

L = 2;        

q = 0.9;      

Time = ceil(double(solve([num2str(T0) '*(0.9)^x = ',num2str(Tend)])));  

count = 0;            

Obj = zeros(Time,1);  

track = zeros(Time,N);

S1 = randperm(N);  

DrawPath(S1,X)    

title('利用自定义函数绘制TSP问题的初始路径(随机产生)—Jason niu')

disp('初始种群中的一个随机值:')

OutputPath(S1);    

Rlength = PathLength(D,S1);

disp(['总距离：',num2str(Rlength)]);

while T0 > Tend    

   count = count + 1;    

   temp = zeros(L,N+1);

   S2 = NewAnswer(S1);  

   [S1,R] = Metropolis(S1,S2,D,T0);

   if count == 1 || R < Obj(count-1)

       Obj(count) = R;          

   else

       Obj(count) = Obj(count-1);

   end

   track(count,:) = S1;

   T0 = q * T0;        

end

figure

plot(1:count,Obj)

xlabel('迭代次数')

ylabel('距离')

title('SA：显示SA算法对TSP问题的优化过程(适应度函数)—Jason niu')

DrawPath(track(end,:),X)

title('SA：利用SA对TSP问题优化，绘制出利用SA算法优化后的最优路径图—Jason niu')

%% IX. 输出最优解的路线和总距离

disp('最优解:')

S = track(end,:);

p = OutputPath(S);

disp(['总距离：',num2str(PathLength(D,S))]);