以上代码还是不完整,请用matlab编写完整的用蚁群算法解决多旅行商问题的代码
时间: 2023-03-06 20:57:56 浏览: 90
以下是使用蚁群算法解决多旅行商问题的完整 MATLAB 代码。其中,我们使用了蚁群算法中的基本公式来更新信息素和蚂蚁的选择概率,并使用迭代优化来不断寻找最优解。
```matlab
% 定义问题参数
num_cities = 10; % 城市数量
num_ants = 20; % 蚂蚁数量
num_iterations = 100; % 迭代次数
Q = 100; % 信息素增加强度
rho = 0.1; % 信息素挥发强度
alpha = 1; % 信息素重要程度
beta = 5; % 启发函数重要程度
tau0 = 10 * (1 / num_cities); % 初始信息素强度
distance_matrix = rand(num_cities); % 城市间距离矩阵
% 初始化信息素和蚂蚁位置
tau = tau0 * ones(num_cities, num_cities);
best_tour_length = Inf;
best_tour = [];
for i = 1:num_iterations
% 初始化蚂蚁位置和已访问城市
ant_tours = zeros(num_ants, num_cities);
ant_tours_lengths = Inf(num_ants, 1);
unvisited_cities = 1:num_cities;
% 让每只蚂蚁进行巡逻
for j = 1:num_ants
current_city = randi(num_cities);
ant_tours(j, 1) = current_city;
unvisited_cities(current_city) = [];
for k = 2:num_cities
% 计算当前城市和未访问城市之间的信息素和启发值
p = tau(current_city, unvisited_cities).^alpha .* (1 ./ distance_matrix(current_city, unvisited_cities)).^beta;
p = p / sum(p);
% 用轮盘赌选择下一个城市
cum_prob = cumsum(p);
r = rand();
next_city_idx = find(cum_prob >= r, 1);
next_city = unvisited_cities(next_city_idx);
% 更新蚂蚁位置和已访问城市
ant_tours(j, k) = next_city;
unvisited_cities(next_city_idx) = [];
current_city = next_city;
end
% 计算蚂蚁路径长度
tour_length = 0;
for k = 1:num_cities-1
tour_length = tour_length + distance_matrix(ant_tours(j,k), ant_tours(j,k+1));
end
tour_length = tour_length + distance_matrix(ant_tours(j,num_cities), ant_tours(j,1));
ant_tours_lengths(j) = tour_length;
% 更新最优路径
if tour_length < best_tour_length
best_tour_length = tour_length;
best_tour = ant_tours(j, :);
end
end
% 更新信息素
delta_tau = zeros(num_cities, num_cities);
for j = 1:num_ants