某公司计划开通一条通勤路线接员工上班。通勤车每天早上从公司出发，经过15个小区（抵达第i个小区时，住在第i个小区的员工上车)，最后回到公司。 假设公司坐标为（0,0)，小区坐标及各小区员工数量见表1. 假设车辆匀速行驶40km/h，员工上车时间忽略不计。 表1 7 小区 1 2 3 4 5 6 8 9 10 11 12 13 14 15 ×坐标 （百米） 35 -13 40 -27 -3 -26 -21 52 39 1 65 -27 13 8 -42 y坐标 （百米） 46 49 -12 18 14 34 40 45 -3 37 35 -14 -19 19 -26 员工人类 1 2 2 3 4 3 3 8 3 2 1 2 3 5 3 在以下不同目标下，寻找相应的最佳通勤车路线: （1）最小化车辆行驶时间。 （2）最小化人均乘车时间。 写出matlab代码

首先，我们需要解析这个问题。这是一个典型的旅行者问题（Traveling Salesman Problem, TSP），我们需要找到一条路径，使得车辆行驶时间最短或者人均乘车时间最短。我们可以使用遗传算法（Genetic Algorithm）来解决这个问题。 接下来，我们将生成Matlab代码： ```matlab % 小区坐标和员工数量 x = [35, -13, 40, -27, -3, -26, -21, 52, 39, 1, 65, -27, 13, 8, -42]; y = [46, 49, -12, 18, 14, 34, 40, 45, -3, 37, 35, -14, -19, 19, -26]; num_employees = [1, 2, 2, 3, 4, 3, 3, 8, 3, 2, 1, 2, 3, 5, 3]; % 计算两点之间的距离 distance = zeros(15, 15); for i = 1:15 for j = 1:15 distance(i, j) = sqrt((x(i) - x(j))^2 + (y(i) - y(j))^2); end end % 遗传算法参数 pop_size = 50; % 种群大小 mutation_rate = 0.1; % 变异率 num_generations = 1000; % 迭代次数 % 目标选择：1表示最小化车辆行驶时间，2表示最小化人均乘车时间 target = 1; % 遗传算法主函数 function [best_route, best_fitness] = genetic_algorithm(target) % 初始化种群 population = init_population(pop_size); for generation = 1:num_generations % 评估种群适应度 fitness = evaluate_fitness(population, target); % 选择操作 selected_indices = selection(fitness); selected_population = population(selected_indices, :); % 交叉操作 offspring_population = crossover(selected_population); % 变异操作 mutated_population = mutation(offspring_population, mutation_rate); % 更新种群 population = mutated_population; % 记录当前最优解 [min_fitness, min_index] = min(fitness); if generation == 1 || min_fitness < best_fitness best_fitness = min_fitness; best_route = population(min_index, :); end end end % 初始化种群 function population = init_population(pop_size) population = zeros(pop_size, 15); for i = 1:pop_size population(i, :) = randperm(15); end end % 评估种群适应度 function fitness = evaluate_fitness(population, target) num_individuals = size(population, 1); fitness = zeros(num_individuals, 1); for i = 1:num_individuals route = population(i, :); total_distance = sum(distance(route(1:end-1), route(2:end))); if target == 1 fitness(i) = total_distance; elseif target == 2 total_time = total_distance / 40; avg_time = total_time / sum(num_employees); fitness(i) = avg_time; end end end % 选择操作 function selected_indices = selection(fitness) num_individuals = size(fitness, 1); selected_indices = zeros(num_individuals, 1); for i = 1:num_individuals % 轮盘赌选择 roulette_wheel = cumsum(fitness / sum(fitness)); random_number = rand(); selected_indices(i) = find(roulette_wheel >= random_number, 1); end end % 交叉操作 function offspring_population = crossover(selected_population) num_individuals = size(selected_population, 1); offspring_population = zeros(num_individuals, 15); for i = 1:2:num_individuals-1 parent1 = selected_population(i, :); parent2 = selected_population(i+1, :); % 部分映射交叉（Partially Mapped Crossover, PMX） crossover_points = sort(randperm(14, 2)); offspring1 = parent1; offspring2 = parent2; for j = crossover_points(1)+1:crossover_points(2) offspring1(j) = parent2(j); offspring2(j) = parent1(j); end for j = 1:crossover_points(1) while any(offspring1(j) == offspring1(crossover_points(1)+1:crossover_points(2))) offspring1(j) = parent2(find(parent1 == offspring1(j), 1)); end while any(offspring2(j) == offspring2(crossover_points(1)+1:crossover_points(2))) offspring2(j) = parent1(find(parent2 == offspring2(j), 1)); end end offspring_population(i, :) = offspring1; offspring_population(i+1, :) = offspring2; end end % 变异操作 function mutated_population = mutation(offspring_population, mutation_rate) num_individuals = size(offspring_population, 1); mutated_population = offspring_population; for i = 1:num_individuals if rand() < mutation_rate % 交换两个随机位置的元素 mutation_points = randperm(14, 2); mutated_population(i, mutation_points) = mutated_population(i, mutation_points(2), mutation_points(1)); end end end % 运行遗传算法求解问题 [best_route, best_fitness] = genetic_algorithm(target); disp(['最佳路线：', num2str(best_route)]); disp(['最佳适应度值：', num2str(best_fitness)]); ``` 这段代码将使用遗传算法求解给定的问题，输出最佳路线和最佳适应度值。你可以根据需要修改`target`变量的值来求解不同的目标。 [2024-04-29 21:09:47 | AI写代码神器 | 1747点数解答]