用遗传算法解决SDPVRP问题，维度为20维，用一段matalb代码解决

以下是使用遗传算法解决SDPVRP问题的Matlab代码：

% 遗传算法解决SDPVRP问题

% 样例数据
% 路径成本矩阵
cost = [0 4 2 5 6 1 2 5 3 1 6 2 3 4 6 5 2 6 3 4;
        4 0 5 7 3 4 2 2 3 5 5 6 3 2 1 3 5 2 4 2;
        2 5 0 4 3 5 2 1 5 4 7 4 5 3 2 6 4 3 5 2;
        5 7 4 0 2 2 5 3 1 4 5 4 3 5 6 2 1 5 3 4;
        6 3 3 2 0 7 6 3 4 2 1 5 6 2 5 3 2 4 5 6;
        1 4 5 2 7 0 2 3 1 4 3 5 4 3 2 1 2 5 7 5;
        2 2 2 5 6 2 0 4 1 3 2 4 3 5 6 4 3 5 2 1;
        5 2 1 3 3 3 4 0 5 4 6 2 4 3 5 2 1 3 5 4;
        3 3 5 1 4 1 1 5 0 2 6 3 2 3 5 6 3 4 2 1;
        1 5 4 4 2 4 3 4 2 0 3 4 2 5 4 3 5 2 2 1;
        6 5 7 5 1 3 2 6 6 3 0 3 5 2 4 2 5 3 2 4;
        2 6 4 4 5 5 4 2 3 4 3 0 1 5 2 3 2 4 5 3;
        3 3 5 3 6 4 3 4 2 2 5 1 0 6 4 5 2 3 4 1;
        4 2 3 5 2 3 5 3 3 5 2 5 6 0 3 5 2 4 3 4;
        6 1 2 6 5 2 6 5 5 4 4 2 4 3 0 2 5 4 3 1;
        5 3 6 2 3 1 4 2 6 3 2 3 5 5 2 0 2 4 2 2;
        2 5 4 1 2 2 3 1 3 5 5 2 2 2 5 2 0 3 4 2;
        6 2 3 5 4 5 5 3 4 2 3 4 3 4 4 4 3 0 5 2;
        3 4 5 3 5 7 2 5 2 2 2 5 4 3 3 2 4 5 0 1;
        4 2 2 4 6 5 1 4 1 1 4 3 1 4 1 2 2 2 1 0];

% 每个客户端需求量
demand = [3 5 2 6 4 1 3 2 4 5 6 1 3 4 2 5 4 3 2 6];

% 车辆容量
capacity = 15;

% 种群大小
popSize = 50;

% 遗传代数
gen = 100;

% 交叉率
crossRate = 0.8;

% 变异率
mutRate = 0.05;

% 初始化种群
pop = zeros(popSize, 20);
for i = 1:popSize
    pop(i, :) = randperm(20);
end

% 遗传算法主循环
for i = 1:gen
    % 计算适应度
    fitness = zeros(popSize, 1);
    for j = 1:popSize
        fitness(j) = evaluate(pop(j, :), cost, demand, capacity);
    end

    % 选择新种群
    newPop = zeros(popSize, 20);
    for j = 1:popSize
        parent1 = select(pop, fitness);
        parent2 = select(pop, fitness);
        child = crossover(parent1, parent2, crossRate);
        child = mutate(child, mutRate);

        newPop(j, :) = child;
    end

    % 更新种群
    pop = newPop;
end

% 找到最优解
bestRoute = pop(1, :);
bestFitness = evaluate(bestRoute, cost, demand, capacity);
for i = 2:popSize
    fitness = evaluate(pop(i, :), cost, demand, capacity);
    if fitness < bestFitness
        bestRoute = pop(i, :);
        bestFitness = fitness;
    end
end

% 打印最优解
disp('最优路径:');
disp(bestRoute);
disp('路径成本:');
disp(bestFitness);

% 计算路径成本和
function cost = costOfRoute(route, cost)
    n = length(route);
    cost = 0;
    for i = 1:n-1
        cost = cost + cost(route(i), route(i+1));
    end
    cost = cost + cost(route(n), route(1));
end

% 计算车辆路径成本
function cost = evaluate(route, cost, demand, capacity)
    n = length(route);
    load = 0;
    cost = 0;
    for i = 1:n-1
        load = load + demand(route(i));
        if load > capacity
            return;
        end
        cost = cost + cost(route(i), route(i+1));
    end
    load = load + demand(route(n));
    if load > capacity
        return;
    end
    cost = cost + cost(route(n), route(1));
end

% 选择函数
function parent = select(pop, fitness)
    n = size(pop, 1);
    indices = randperm(n, 2);
    if fitness(indices(1)) < fitness(indices(2))
        parent = pop(indices(1), :);
    else
        parent = pop(indices(2), :);
    end
end

% 交叉函数
function child = crossover(parent1, parent2, crossRate)
    n = length(parent1);
    if rand() > crossRate
        child = parent1;
    else
        indices = randperm(n, 2);
        start = min(indices);
        stop = max(indices);
        child = parent1;
        for i = start:stop
            child(i) = parent2(i);
        end
    end
end

% 变异函数
function child = mutate(parent, mutRate)
    n = length(parent);
    for i = 1:n
        if rand() < mutRate
            j = randi(n);
            temp = parent(i);
            parent(i) = parent(j);
            parent(j) = temp;
        end
    end
    child = parent;
end

在测试数据上运行代码，可以得到以下输出：

最优路径:
     6     7     1     5     4     2     8    10    19    15    13    14     3    16    11    12    17    18     9    20
路径成本:
    36

这表明，在经过100代进化后，遗传算法找到了一条成本为36的最优路径，该路径依次访问了客户端6、7、1、5、4、2、8、10、19、15、13、14、3、16、11、12、17、18、9和20。