想要基于栅格法的蚁群算法的路径规划的matlab代码

以下是基于栅格法的蚁群算法的路径规划的 Matlab 代码：

% 定义地图和起点、终点
map = [0 0 0 0 0 0 0 0 0 0;
       0 1 1 1 0 1 0 0 0 0;
       0 0 0 1 0 1 0 1 0 0;
       0 1 0 0 0 1 0 1 0 0;
       0 1 1 1 1 1 1 1 1 0;
       0 1 0 0 0 0 0 0 1 0;
       0 1 0 1 1 1 1 0 1 0;
       0 1 0 0 0 0 0 0 1 0;
       0 1 1 1 1 1 1 1 1 0;
       0 0 0 0 0 0 0 0 0 0];
startPoint = [2,2];
endPoint = [9,9];

% 初始化蚂蚁群
antCount = 20;
antPosition = repmat(startPoint, antCount, 1);
pheromone = zeros(size(map));
alpha = 1;
beta = 5;
Q = 1;
evaporationRate = 0.5;
maxIteration = 100;

% 迭代寻找最短路径
for iteration = 1:maxIteration
    % 遍历所有蚂蚁，更新蚂蚁位置
    for ant = 1:antCount
        currentPosition = antPosition(ant,:);
        while ~isequal(currentPosition, endPoint)
            % 计算当前位置周围的邻居节点
            neighbors = [];
            for i = -1:1
                for j = -1:1
                    if i ~= 0 || j ~= 0
                        neighborPosition = currentPosition + [i,j];
                        if neighborPosition(1) >= 1 && neighborPosition(1) <= size(map,1) && ...
                           neighborPosition(2) >= 1 && neighborPosition(2) <= size(map,2) && ...
                           map(neighborPosition(1), neighborPosition(2)) == 0
                            neighbors(end+1,:) = neighborPosition;
                        end
                    end
                end
            end
            
            % 计算当前位置到邻居节点的转移概率
            transitionProbability = zeros(size(neighbors,1), 1);
            for i = 1:size(neighbors,1)
                neighborPosition = neighbors(i,:);
                if pheromone(neighborPosition(1), neighborPosition(2)) > 0
                    transitionProbability(i) = pheromone(currentPosition(1), currentPosition(2))^alpha * ...
                                                (1/distance(currentPosition, neighborPosition))^beta;
                else
                    transitionProbability(i) = 0;
                end
            end
            transitionProbability = transitionProbability/sum(transitionProbability);
            
            % 选择下一个位置
            cumProbability = cumsum(transitionProbability);
            randomValue = rand;
            for i = 1:size(cumProbability,1)
                if randomValue <= cumProbability(i)
                    currentPosition = neighbors(i,:);
                    break;
                end
            end
            
            % 更新蚂蚁位置
            antPosition(ant,:) = currentPosition;
        end
    end
    
    % 计算每只蚂蚁的路径长度和贡献度
    pathLength = zeros(antCount, 1);
    contribution = zeros(size(map));
    for ant = 1:antCount
        pathLength(ant) = distance(startPoint, antPosition(ant,:));
        for i = 1:size(antPosition,1)
            contribution(antPosition(i,1), antPosition(i,2)) = contribution(antPosition(i,1), antPosition(i,2)) + Q/pathLength(ant);
        end
    end
    
    % 更新信息素
    pheromone = (1-evaporationRate).*pheromone + contribution;
    
    % 如果找到终点，提前结束迭代
    if any(ismember(antPosition, endPoint, 'rows'))
        break;
    end
end

% 输出最短路径
currentPosition = startPoint;
shortestPath = [currentPosition];
while ~isequal(currentPosition, endPoint)
    neighbors = [];
    for i = -1:1
        for j = -1:1
            if i ~= 0 || j ~= 0
                neighborPosition = currentPosition + [i,j];
                if neighborPosition(1) >= 1 && neighborPosition(1) <= size(map,1) && ...
                   neighborPosition(2) >= 1 && neighborPosition(2) <= size(map,2) && ...
                   map(neighborPosition(1), neighborPosition(2)) == 0
                    neighbors(end+1,:) = neighborPosition;
                end
            end
        end
    end
    
    cumProbability = zeros(size(neighbors,1), 1);
    for i = 1:size(neighbors,1)
        if ismember(neighbors(i,:), shortestPath, 'rows')
            cumProbability(i) = 0;
        else
            cumProbability(i) = pheromone(currentPosition(1), currentPosition(2))^alpha * ...
                                 (1/distance(currentPosition, neighbors(i,:)))^beta;
        end
    end
    cumProbability = cumProbability/sum(cumProbability);
    
    cumProbability = cumsum(cumProbability);
    randomValue = rand;
    for i = 1:size(cumProbability,1)
        if randomValue <= cumProbability(i)
            currentPosition = neighbors(i,:);
            shortestPath(end+1,:) = currentPosition;
            break;
        end
    end
end

% 绘制地图和最短路径
figure();
hold on;
for i = 1:size(map,1)
    for j = 1:size(map,2)
        if map(i,j) == 1
            rectangle('Position', [j-1, size(map,1)-i, 1, 1], 'FaceColor', 'k');
        end
    end
end
plot(startPoint(2)-0.5, size(map,1)-startPoint(1)+0.5, 'ro', 'MarkerFaceColor', 'r');
plot(endPoint(2)-0.5, size(map,1)-endPoint(1)+0.5, 'gx', 'MarkerFaceColor', 'g');
shortestPathX = shortestPath(:,2)-0.5;
shortestPathY = size(map,1)-shortestPath(:,1)+0.5;
plot(shortestPathX, shortestPathY, 'b--', 'LineWidth', 2);
xlim([0, size(map,2)]);
ylim([0, size(map,1)]);
axis equal;

在这个代码中，我们首先定义了地图和起点、终点。然后，我们初始化了一个包含多只蚂蚁的蚂蚁群，并定义了信息素、alpha、beta、Q、蒸发速率以及最大迭代次数等参数。接下来，我们开始迭代寻找最短路径。在每次迭代中，我们首先遍历所有蚂蚁，更新它们的位置。然后，我们计算每只蚂蚁的路径长度和贡献度，并根据它们的贡献度更新信息素。最后，我们根据信息素和路径长度计算每个邻居节点的转移概率，选择下一个位置，并更新蚂蚁的位置。如果找到了终点，就提前结束迭代。最后，我们输出最短路径并绘制地图和最短路径。