写一个matlab基于鲸鱼算法求解有起点的带容量的车辆路径问题的代码

以下是一个基于鲸鱼算法求解有起点的带容量的车辆路径问题的Matlab代码。这个代码使用了TSP（旅行商问题）作为例子，但是可以通过修改一些参数来适应带容量车辆路径问题。

% 定义鲸鱼算法的参数
whale_num = 10; % 鲸鱼的数量
max_iter = 100; % 最大迭代次数
a = 2; % 控制参数
b = 0.5; % 控制参数

% 定义TSP问题的参数
city_num = 50; % 城市数量
capacity = 100; % 车辆容量
demand = 1 + floor(9*rand(city_num-1,1)); % 需求量
distance_matrix = rand(city_num) + eye(city_num); % 距离矩阵

% 初始化鲸鱼位置和速度
position = rand(whale_num,city_num+1)*city_num;
velocity = rand(whale_num,city_num+1)*city_num;

% 迭代寻找最优解
best_solution = zeros(max_iter,1); % 存储每次迭代的最优解
best_route = zeros(max_iter,city_num); % 存储每次迭代的最优路径
for iter = 1:max_iter
    % 计算每只鲸鱼的适应度值
    fitness = zeros(whale_num,1);
    for whale = 1:whale_num
        route = position(whale,:);
        route(route == 0) = [];
        if length(route) > 1
            % 计算路径长度
            d = 0;
            for i = 1:length(route)-1
                d = d + distance_matrix(route(i),route(i+1));
            end
            % 计算路径是否符合容量限制
            load = 0;
            for i = 2:length(route)
                load = load + demand(route(i)-1);
                if load > capacity
                    d = Inf;
                    break;
                end
            end
            fitness(whale) = 1/d;
        end
    end
    % 寻找最优解和路径
    [best_fitness,index] = max(fitness);
    best_route(iter,:) = position(index,:);
    best_solution(iter) = 1/best_fitness;
    % 更新鲸鱼位置和速度
    for whale = 1:whale_num
        route = position(whale,:);
        route(route == 0) = [];
        if length(route) == 1
            % 随机选择一个城市作为起点
            current = route(1);
            unvisited = 1:city_num;
            unvisited(current) = [];
            next = unvisited(randi(city_num-1));
            position(whale,2:end) = next;
        else
            % 计算鲸鱼的新位置和速度
            r1 = rand;
            r2 = rand;
            A = 2*a*r1 - a;
            C = 2*r2;
            route_best = best_route(iter,:);
            route_best(route_best == 0) = [];
            route_current = route;
            route_current(route_current == 0) = [];
            X1 = route_current;
            X2 = route_best;
            X3 = position(randi(whale_num),:);
            velocity(whale,:) = abs(X1 - position(whale,:)) .* exp(b .* A) .* sin(2 .* pi .* A) + ...
                abs(X2 - position(whale,:)) .* exp(b .* A) .* sin(2 .* pi .* A) + ...
                abs(X3 - position(whale,:)) .* exp(b .* A) .* sin(2 .* pi .* A);
            position(whale,:) = position(whale,:) + velocity(whale,:);
            % 随机选择一个城市作为下一个节点
            current = route(end);
            unvisited = 1:city_num;
            unvisited(current) = [];
            next = unvisited(randi(city_num-1));
            position(whale,end) = next;
        end
    end
end

% 绘制最优路径
best_route(best_route == 0) = [];
figure;
plot(best_route,'-o');
title('Best Route');

% 绘制最优解收敛曲线
figure;
plot(best_solution);
title('Best Solution Convergence');

需要注意的是，这个代码只是一个示例，需要根据实际问题进行修改和调整。特别是在定义TSP问题的参数时，需要根据实际情况进行设置。