蚁群算法路径规划代码matlab

蚁群算法是一种模拟蚂蚁觅食行为的优化算法，常用于解决路径规划问题。下面是一个简单的用 MATLAB 实现蚁群算法进行路径规划的示例代码：

% 蚁群算法参数设置
numAnts = 50; % 蚂蚁数量
numIterations = 100; % 迭代次数
alpha = 1; % 信息素重要程度因子
beta = 5; % 启发函数重要程度因子
rho = 0.5; % 信息素蒸发系数
Q = 100; % 信息素增量常数
initialPheromone = 0.1; % 初始信息素浓度

% 城市坐标
cities = [0, 0; 1, 1; 2, 0; 3, 1; 4, 0];

% 计算城市间距离矩阵
numCities = size(cities, 1);
distances = pdist2(cities, cities);

% 初始化信息素矩阵
pheromones = initialPheromone * ones(numCities, numCities);

% 迭代寻找最优路径
bestDistance = Inf;
bestPath = zeros(1, numCities + 1);
for iteration = 1:numIterations
    antPaths = zeros(numAnts, numCities + 1); % 记录每只蚂蚁的路径
    antDistances = zeros(numAnts, 1); % 记录每只蚂蚁的路径长度
    
    % 每只蚂蚁根据信息素和启发函数选择下一个城市
    for ant = 1:numAnts
        visited = zeros(1, numCities); % 记录已访问城市
        currentCity = randi(numCities); % 随机选择初始城市
        visited(currentCity) = 1;
        antPaths(ant, 1) = currentCity;
        
        for step = 2:numCities
            unvisited = find(visited == 0); % 未访问城市的索引
            probabilities = pheromones(currentCity, unvisited).^alpha .* (1 ./ distances(currentCity, unvisited)).^beta;
            probabilities = probabilities / sum(probabilities);
            
            nextCity = randsample(unvisited, 1, true, probabilities);
            antPaths(ant, step) = nextCity;
            visited(nextCity) = 1;
            currentCity = nextCity;
        end
        
        % 计算蚂蚁路径长度
        antPaths(ant, end) = antPaths(ant, 1); % 回到起点
        antDistances(ant) = sum(distances(sub2ind([numCities, numCities], antPaths(ant, 1:end-1), antPaths(ant, 2:end))));
        
        % 更新最优路径
        if antDistances(ant) < bestDistance
            bestDistance = antDistances(ant);
            bestPath = antPaths(ant, :);
        end
    end
    
    % 更新信息素矩阵
    deltaPheromones = zeros(numCities, numCities);
    for ant = 1:numAnts
        for step = 1:numCities
            deltaPheromones(antPaths(ant, step), antPaths(ant, step+1)) = deltaPheromones(antPaths(ant, step), antPaths(ant, step+1)) + Q / antDistances(ant);
        end
    end
    pheromones = (1 - rho) * pheromones + deltaPheromones;
end

% 输出最优路径
disp('Best path:');
disp(bestPath);
disp('Best distance:');
disp(bestDistance);

以上代码实现了蚁群算法的基本思路，使用随机选择的方式进行路径选择，通过更新信息素矩阵来模拟信息传递和蒸发的过程。你可以根据自己的需求进行适当的修改和优化。注意，这只是一个简单的示例代码，可能还需要根据具体问题进行调整。