AG-TSP.zip_AG genetic_tsp_tsp ag_遗传算法 _遗传算法 TSP

%遗传算法TSP问题 clear clc %城市的坐标 X=[16.47 96.10; 16.47 94.44; 20.09 92.54; 22.39 93.37; 25.23 97.24; 22.00 96.05; 20.47 97.02; 17.20 96.29; 16.30 97.38; 14.05 98.12; 16.53 97.38; 21.52 95.59; 19.41 97.13; 20.09 92.55]; sizepop=100; %种群大小 maxgen=200; %迭代次数 pc=0.9; %交叉概率 pm=0.5; %变异概率 ps=0.9; %选择概率 D=Distance(X); %生成距离矩阵 N=size(X,1); %城市的个数 bestfitness=[]; bestchrom=[]; meanfitness=[]; trace=[];%记录每次迭代后，最优适应度，平均适应度 %=================初始化种群=============== chrom=Initpop(N,sizepop); fitness=Fitness(D,chrom); [bestfitness,indexbest]=min(fitness); meanfitness=sum(fitness)/sizepop; trace=[trace;bestfitness,meanfitness]; %================在二维图上画出所有坐标点===== plot_route(X,chrom(1,:),1) %================进行优化迭代============== for i=1:maxgen %选择 selchrom=Select(chrom,fitness,ps); %交叉 selchrom=Cross(selchrom,pc); %变异 selchrom=Mutation(selchrom,pm); %进化逆转 selchrom=Reverse(selchrom,D); %重插入子代的新物种 chrom=backsize(chrom,selchrom,fitness); fitness=Fitness(D,chrom); [bestfitness,indexbest]=min(fitness); meanfitness=sum(fitness)/sizepop; bestchrom=chrom(indexbest,:); trace=[trace;bestfitness,meanfitness]; end plot_route(X,bestchrom,2)