主程序如下：
clc;
clear;
CITYSIZE = 10; % 城市个数
POPSIZE = 50; % 种群个数
PC = 0.4; % 交叉概率
PM = 0.05; % 变异概率
MAXGEN = 150; % 迭代次数
LEAVING = 5; % 父代保留数量
gen = 0;
bestfit = zeros(1, MAXGEN);
bestlength = zeros(1, MAXGEN);
pos = [1 2 2 3 1 4 5 5 6 4; 1 1 2 2 3 4 4 5 5 6]; % 城市坐标
D = distancematrix(pos); % 城市距离矩阵
pop = initpop(POPSIZE, CITYSIZE);
len = callength(D, pop);
fit = calfitness(len);
% 优化
while gen < MAXGEN
childpop = selection(pop, fit, LEAVING); % 选择
leavingpop = selection(pop, fit, POPSIZE-LEAVING);
pop = [leavingpop; childpop]; % 保留一部分父代
pop = crossover(pop, PC); % 交叉
pop = mutation(pop, PM); % 变异
gen = gen + 1;
len = callength(D, pop);
fit = calfitness(len);
bestindex = bestindividual(fit);
bestfit(1, gen) = fit(bestindex);
bestlength(1, gen) = len(bestindex);
end
figure(1);
plot(1:MAXGEN, bestfit(1,:));
xlabel('进化代数');
ylabel('最优适应度值');
title('最优适应度值图');
grid on;
figure(2);
plot(1:MAXGEN, bestlength(1,:));
xlabel('进化代数');
ylabel('最优距离');
title('最优距离图');
grid on;
figure(3);
plot_route(pos, pop(bestindex,:));
grid on;
执行结果如下：
GA解TSP问题路径图
GA解TSP问题适应度值图
GA解TSP问题最优距离图
