Mgasa混合遗传模拟退火算法解决TSP问题

load('Location'); initial_popSize=50; %初始种群规模 numberofcities=length(Location); %基因数量 StopTolerance=1; %终止温度 TempRatio=0.5; %降温系数 Temp0=70; %初始温度 i=1; Temp=Temp0; %温度 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% 生成距离矩阵 %%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% dis=zeros(numberofcities,numberofcities); for i=1:numberofcities for j=1:numberofcities dis(i,j)=norm(Location(i,:)-Location(j,:)); end end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% 初始种群 %%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% popSize = initial_popSize; %种群规模 max_generation=300; %初始化最大种群代数 pop=zeros(popSize,numberofcities); for i = 1:popSize %初始化种群 pop(i,:) = randperm(numberofcities); end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% 模拟退火 %%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% i=1; while Temp>StopTolerance disp(Temp); fitness =mgasa_fitness(pop,dis); %计算适应度(距离) pop=mgasa_annealing(Temp,pop,dis,max_generation,fitness,popSize); Temp=Temp0/i; i=i+1; end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% 得到最佳解 %%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% fitness =ga_fitness(pop,dis); %计算适应度(距离) [maxfitness, index] = max(fitness); %找出最短距离 optRoute = pop(index,:); %找出最短距离对就的路径 Location_opt=zeros(numberofcities,2); for i=1:numberofcities Location_opt(i,:)=Location(optRoute(i),:); end line(Location_opt(:,1),Location_opt(:,2)); hold on; line([Location_opt(1,1),Location_opt(numberofcities,1)],... [Location_opt(1,2),Location_opt(numberofcities,2)]); disp(maxfitness);