请基于遗传算法写一段多式联运路径优化matlab算法，目标函数为中转和运输时间加中转和运输费用最低

遗传算法是一种基于自然选择和遗传机制的优化算法，可以用于解决多式联运路径优化问题。以下是一个基于遗传算法的多式联运路径优化matlab算法的示例：

首先，定义问题的目标函数，即中转和运输时间加中转和运输费用最低：

function [f] = objective_function(x)
% x为决策变量，表示每个物流节点的运输路径
% f为目标函数值，即中转和运输时间加中转和运输费用最低
    % 计算中转和运输时间
    time = 0;
    for i = 1:length(x)-1
        time = time + get_time(x(i), x(i+1));
    end
    
    % 计算中转和运输费用
    cost = 0;
    for i = 1:length(x)-1
        cost = cost + get_cost(x(i), x(i+1));
    end
    
    % 计算目标函数值
    f = time + cost;
end

然后，定义遗传算法的参数和操作，包括初始种群生成、适应度计算、选择、交叉和变异：

function [x_best, f_best] = ga_algorithm(num_iterations, num_population, num_genes)
% num_iterations为迭代次数
% num_population为种群大小
% num_genes为每个个体的基因数，即物流节点数

    % 初始化种群
    population = randi([1, num_genes], num_population, num_genes);
    
    % 迭代优化
    for iter = 1:num_iterations
        
        % 计算适应度
        fitness = zeros(num_population, 1);
        for i = 1:num_population
            fitness(i) = objective_function(population(i, :));
        end
        
        % 找到最优解
        [f_best, idx_best] = min(fitness);
        x_best = population(idx_best, :);
        
        % 选择操作
        population_new = zeros(num_population, num_genes);
        for i = 1:num_population
            idx1 = randi(num_population);
            idx2 = randi(num_population);
            if fitness(idx1) < fitness(idx2)
                population_new(i, :) = population(idx1, :);
            else
                population_new(i, :) = population(idx2, :);
            end
        end
        
        % 交叉操作
        for i = 1:2:num_population
            idx1 = randi(num_population);
            idx2 = randi(num_population);
            crossover_point = randi(num_genes-1);
            population_new(i, :) = [population(idx1, 1:crossover_point), population(idx2, crossover_point+1:end)];
            population_new(i+1, :) = [population(idx2, 1:crossover_point), population(idx1, crossover_point+1:end)];
        end
        
        % 变异操作
        for i = 1:num_population
            if rand() < 0.01
                mutation_point = randi(num_genes);
                population_new(i, mutation_point) = randi(num_genes);
            end
        end
        
        % 更新种群
        population = population_new;
        
    end
end

最后，定义一些辅助函数，包括获取两个物流节点之间的运输时间和费用：

function [t] = get_time(node1, node2)
% node1和node2为两个物流节点
% t为运输时间
    % TODO: 根据实际情况计算运输时间
    t = rand() * 10;
end

function [c] = get_cost(node1, node2)
% node1和node2为两个物流节点
% c为运输费用
    % TODO: 根据实际情况计算运输费用
    c = rand() * 100;
end

使用示例：

num_iterations = 100;
num_population = 50;
num_genes = 10;

[x_best, f_best] = ga_algorithm(num_iterations, num_population, num_genes);
disp(['最优解：', num2str(x_best)]);
disp(['目标函数值：', num2str(f_best)]);