基于遗传算法的物流路径优化问题

% 定义染色体长度和种群数量
chromosome_length = 10;
population_size = 50;
% 生成随机的初始种群
population = round(rand(population_size, chromosome_length));
% 定义适应度函数
fitness = zeros(population_size, 1);
for i = 1:population_size
    % 计算适应度值
    fitness(i) = fitness_function(population(i,:));
end
% 定义迭代次数
max_generation = 500;
% 进化过程
for t = 1:max_generation
    % 选择操作
    p = fitness/sum(fitness);
    q = cumsum(p);
    temp_population = zeros(population_size, chromosome_length);
    for i = 1:population_size
        r = rand();
        idx = find(q >= r, 1);
        temp_population(i,:) = population(idx,:);
    end
    % 交叉操作
    for i = 1:2:population_size
        r = rand();
        if r < 0.6
            crossover_index = randi([1, chromosome_length-1]);
            parent1 = temp_population(i,:);
            parent2 = temp_population(i+1,:);
            temp_population(i,:) = [parent1(1:crossover_index), parent2(crossover_index+1:end)];
            temp_population(i+1,:) = [parent2(1:crossover_index), parent1(crossover_index+1:end)];
        end
    end
    % 变异操作
    for i = 1:population_size
        r = rand();
        if r < 0.05
            mutation_index = randi([1, chromosome_length]);
            temp_population(i,mutation_index) = 1 - temp_population(i,mutation_index);
        end
    end
    % 更新种群
    population = temp_population;
    % 计算适应度值
    for i = 1:population_size
        fitness(i) = fitness_function(population(i,:));
    end
end
% 输出最优解
[max_fitness, idx] = max(fitness);
best_individual = population(idx,:);
disp(['Best fitness: ', num2str(max_fitness)]);
disp(['Best individual: ', num2str(best_individual)]);