tspga.m

tsp（旅行商问题） 利用matlab遗传算法、模拟退火算法以及lingo动态规划求解

locations= [1304 2312;3639 1315;4177 2244;3712 1399;3488 1535;3326 1556;
                3238 1229;4196 1044;4312  790;4386  570;3007 1970;2562 1756;
                 2788 1491;2381 1676;1332  695;3715 1678;3918 2179;4061 2370;
                 3780 2212;3676 2578;4029 2838;4263 2931;3429 1908;3507 2376;             
                 3394 2643;3439 3201;2935 3240;3140 3550;2545 2357;2778 2826;2370 2975];

plot(locations(:,1),locations(:,2),'bo');
n= size(locations,1);%n表示问题的规模（城市个数）
distances = zeros(n);
for count1=1:n,
    for count2=1:count1,
        x1 = locations(count1,1);
        y1 = locations(count1,2);
        x2 = locations(count2,1);
        y2 = locations(count2,2);
        distances(count1,count2)=sqrt((x1-x2)^2+(y1-y2)^2);
        distances(count2,count1)=distances(count1,count2);
    end;
end;
type tsp_create_permutations.m
type tsp_crossover_permutation.m
type tsp_mutate_permutation.m
type tsp_fitness.m
type tsp_traveling_salesman_plot.m
FitnessFcn = @(x) tsp_fitness(x,distances);
tsp_my_plot = @(options,state,flag) tsp_traveling_salesman_plot(options, ...
            state,flag,locations);
options = gaoptimset('PopulationType', 'custom','PopInitRange', ...
                     [1;n]);
options = gaoptimset(options,'CreationFcn',@tsp_create_permutations, ...
                             'CrossoverFcn',@tsp_crossover_permutation, ...
                             'MutationFcn',@tsp_mutate_permutation, ...
                             'PlotFcn', tsp_my_plot, ...
                             'Generations',1000,'PopulationSize',60, ...
                             'StallGenLimit',200,'Vectorized','on');
numberOfVariables = n;
[x,fval,reason,output] = ga(FitnessFcn,numberOfVariables,options)