蚁群算法路径规划matlab代码
时间: 2023-06-17 17:07:29 浏览: 41
以下是一个简单的蚁群算法路径规划的Matlab代码示例:
```matlab
clear
clc
% 初始化参数
num_ants = 20; % 蚂蚁数量
num_cities = 10; % 城市数量
alpha = 1; % 信息素重要程度因子
beta = 5; % 启发函数重要程度因子
rho = 0.1; % 信息素挥发速度
Q = 1; % 常系数
num_iters = 100; % 迭代次数
% 生成随机地图
map = rand(num_cities, 2);
% 计算城市距离矩阵
dist_mat = pdist2(map, map);
% 初始化信息素矩阵
pheromone_mat = ones(num_cities, num_cities);
% 开始迭代
for iter = 1:num_iters
% 初始化蚂蚁位置和路径
ant_pos = randi(num_cities, num_ants, 1);
ant_path = zeros(num_ants, num_cities);
% 计算蚂蚁路径
for i = 1:num_cities-1
for j = 1:num_ants
% 计算可行路径概率
prob = pheromone_mat(ant_pos(j), :) .^ alpha .* (1 ./ dist_mat(ant_pos(j), :)) .^ beta;
prob(ant_path(j, :)) = 0;
prob = prob / sum(prob);
% 轮盘赌选择下一个城市
next_pos = randsrc(1, 1, [1:num_cities; prob]);
ant_path(j, i+1) = next_pos;
ant_pos(j) = next_pos;
end
end
% 计算路径距离和适应度
path_dist = zeros(num_ants, 1);
for i = 1:num_ants
path = ant_path(i, :);
path_dist(i) = sum(dist_mat(sub2ind([num_cities, num_cities], path(1:end-1), path(2:end))));
end
fitness = 1 ./ path_dist;
% 更新信息素矩阵
delta_pheromone = zeros(num_cities, num_cities);
for i = 1:num_ants
for j = 1:num_cities-1
delta_pheromone(ant_path(i, j), ant_path(i, j+1)) = delta_pheromone(ant_path(i, j), ant_path(i, j+1)) + Q/ path_dist(i);
end
end
pheromone_mat = (1-rho) * pheromone_mat + delta_pheromone;
% 输出结果
[best_fitness, best_idx] = max(fitness);
best_path = ant_path(best_idx, :);
fprintf('Iteration %d: Best path length = %f\n', iter, 1/best_fitness);
plot(map(:,1), map(:,2), 'ro');
hold on;
plot(map(best_path, 1), map(best_path, 2), 'b-');
hold off;
pause(0.1);
end
```
这个代码实现了简单的蚁群算法路径规划,包括随机地图的生成、城市距离矩阵的计算、信息素矩阵的初始化、蚂蚁路径的计算、路径距离和适应度的计算、信息素矩阵的更新等步骤。
你可以根据自己的需要进行修改和优化。