写一段matlab代码，仿真蚁群算法路径规划

好的，以下是一段使用蚁群算法进行路径规划的 Matlab 代码：

% 蚁群算法路径规划
clear;
close all;
clc;

% 假设我们的地图是一个正方形网络，以下是设置参数
num_col = 30; % 列数
num_row = 30; % 行数
num_nodes = num_col * num_row; % 总节点数
num_ants = 50; % 蚂蚁数
alpha = 1; % 信息素重要程度
beta = 3; % 启发式信息重要程度
rho = 0.5; % 信息素挥发因子
Q = 10; % 信息素增量系数
max_iter = 100; % 最大迭代次数

% 初始化节点距离和信息素矩阵
dist_matrix = zeros(num_nodes, num_nodes);
pheromone_matrix = ones(num_nodes, num_nodes);

% 计算节点距离矩阵
for i = 1:num_nodes
    for j = 1:num_nodes
        if i == j
            dist_matrix(i, j) = 0;
        else
            col_i = mod(i-1, num_col) + 1;
            row_i = ceil(i/num_col);
            col_j = mod(j-1, num_col) + 1;
            row_j = ceil(j/num_col);
            dist_matrix(i, j) = norm([col_i, row_i] - [col_j, row_j]);
        end
    end
end

% 开始迭代
best_path = [];
best_len = inf;
for iter = 1:max_iter
    
    % 初始化蚂蚁的位置
    ant_pos = randi(num_nodes, num_ants, 1);
    
    % 进行蚁群搜索过程
    for step = 1:num_nodes-1
        % 计算每只蚂蚁当前位置与其他未走过的节点之间的信息素和启发式信息
        prob_matrix = zeros(num_nodes, num_nodes);
        for m = 1:num_ants
            current_node = ant_pos(m);
            unvisited_nodes = setdiff(1:num_nodes, ant_pos(m:end));
            for n = unvisited_nodes
                prob_matrix(current_node, n) = pheromone_matrix(current_node, n)^alpha ...
                    * (1/dist_matrix(current_node, n))^beta;
            end
        end
        % 选取下一个节点
        [max_prob, next_node] = max(prob_matrix(:));
        [ant_idx, ~] = find(prob_matrix == max_prob, 1, 'first');
        ant_pos(ant_idx, step+1) = next_node;
    end
    
    % 计算本次迭代的最短路径
    path_len = zeros(num_ants, 1);
    for m = 1:num_ants
        path_len(m) = sum(dist_matrix(sub2ind([num_row, num_col], ...
            mod(ant_pos(m,1:end-1)-1,num_row)+1, ...
            floor((ant_pos(m,1:end-1)-1)/num_row)+1), ...
            sub2ind([num_row, num_col], ...
            mod(ant_pos(m,2:end)-1,num_row)+1, ...
            floor((ant_pos(m,2:end)-1)/num_row)+1)));
        if path_len(m) < best_len
            best_len = path_len(m);
            best_path = ant_pos(m, :);
        end
    end
    
    % 更新信息素矩阵
    delta_pheromone = zeros(num_nodes, num_nodes);
    for m = 1:num_ants
        for n = 1:num_nodes-1
            current_node = ant_pos(m, n);
            next_node = ant_pos(m, n+1);
            delta_pheromone(current_node, next_node) = delta_pheromone(current_node, next_node) + Q/path_len(m);
        end
    end
    pheromone_matrix = (1-rho)*pheromone_matrix + delta_pheromone;
    
end

% 输出结果
disp(['最短路径长度：', num2str(best_len)]);
disp(['最短路径：', num2str(best_path)]);

% 绘图
figure;
hold on;
for i = 1:num_nodes
    col_i = mod(i-1, num_col) + 1;
    row_i = ceil(i/num_col);
    for j = i+1:num_nodes
        if dist_matrix(i, j) < 2
            col_j = mod(j-1, num_col) + 1;
            row_j = ceil(j/num_col);
            plot([col_i, col_j], [row_i, row_j], '-b');
        end
    end
end
for n = 1:num_nodes-1
    current_node = best_path(n);
    next_node = best_path(n+1);
    col_i = mod(current_node-1, num_col) + 1;
    row_i = ceil(current_node/num_col);
    col_j = mod(next_node-1, num_col) + 1;
    row_j = ceil(next_node/num_col);
    plot([col_i, col_j], [row_i, row_j], '-r', 'LineWidth', 2);
end
title('蚁群算法路径规划');
xlabel('列');
ylabel('行');
axis equal;
axis([0 num_col+1 0 num_row+1]);