遗传模拟退火算法对流水车间调度优化，目标为最小化最大流程时间，输出甘特图和收敛图，matlab完整代码如何编写

以下是遗传模拟退火算法对流水车间调度优化的matlab完整代码，包含甘特图和收敛图的输出：

% 遗传模拟退火算法对流水车间调度优化
% 目标为最小化最大流程时间

clc;
clear;

% 设置参数
job_num = 10; % 工件数目
machine_num = 3; % 机器数目
pop_size = 50; % 种群大小
max_gen = 100; % 最大迭代次数
pc = 0.8; % 交叉概率
pm = 0.1; % 变异概率
T0 = 100; % 初始温度
Tf = 1; % 最终温度
alpha = 0.98; % 降温系数

% 生成初始种群
pop = zeros(pop_size, job_num);
for i = 1:pop_size
    pop(i, :) = randperm(job_num);
end

% 计算初始种群适应度
fitness = zeros(pop_size, 1);
for i = 1:pop_size
    [makespan, ~] = schedule(pop(i, :), job_num, machine_num);
    fitness(i) = makespan;
end

% 记录最优解和最优解对应的适应度
best_sol = pop(1, :);
best_fitness = fitness(1);

% 记录每一代的最优解和平均适应度
best_fitness_each_gen = zeros(max_gen, 1);
avg_fitness_each_gen = zeros(max_gen, 1);

% 记录退火过程中的最优解和最优解对应的适应度
sa_best_sol = pop(1, :);
sa_best_fitness = fitness(1);

% 记录退火过程中每一步的最优解和最优解对应的适应度
sa_best_fitness_each_step = zeros(max_gen, 1);

% 迭代
for gen = 1:max_gen
    % 交叉
    for i = 1:2:pop_size
        if rand < pc
            % 选择父代
            p1 = tournament_selection(pop, fitness, 2);
            p2 = tournament_selection(pop, fitness, 2);
            % 交叉
            [c1, c2] = crossover(p1, p2);
            % 更新种群
            pop(i, :) = c1;
            pop(i+1, :) = c2;
        end
    end
    
    % 变异
    for i = 1:pop_size
        if rand < pm
            % 变异
            c = mutation(pop(i, :));
            % 更新种群
            pop(i, :) = c;
        end
    end
    
    % 计算种群适应度
    for i = 1:pop_size
        [makespan, ~] = schedule(pop(i, :), job_num, machine_num);
        fitness(i) = makespan;
    end
    
    % 选择
    [pop, fitness] = elitist_selection(pop, fitness, pop_size);
    
    % 记录最优解和最优解对应的适应度
    if fitness(1) < best_fitness
        best_sol = pop(1, :);
        best_fitness = fitness(1);
    end
    
    % 记录每一代的最优解和平均适应度
    best_fitness_each_gen(gen) = best_fitness;
    avg_fitness_each_gen(gen) = mean(fitness);
    
    % 退火
    T = T0 * alpha^(gen-1); % 当前温度
    for i = 1:pop_size
        % 选择邻域解
        c = pop(i, :);
        neighbor = mutation(c);
        % 计算邻域解适应度
        [neighbor_makespan, ~] = schedule(neighbor, job_num, machine_num);
        % 计算适应度差
        delta_fitness = neighbor_makespan - fitness(i);
        % 判断是否接受邻域解
        if delta_fitness < 0 || rand < exp(-delta_fitness/T)
            pop(i, :) = neighbor;
            fitness(i) = neighbor_makespan;
        end
        % 记录退火过程中的最优解和最优解对应的适应度
        if neighbor_makespan < sa_best_fitness
            sa_best_sol = neighbor;
            sa_best_fitness = neighbor_makespan;
        end
    end
    
    % 记录退火过程中每一步的最优解和最优解对应的适应度
    sa_best_fitness_each_step(gen) = sa_best_fitness;
end

% 输出结果
fprintf('最优解：\n');
disp(best_sol);
fprintf('最优解对应的适应度：\n');
disp(best_fitness);
fprintf('退火过程中的最优解：\n');
disp(sa_best_sol);
fprintf('退火过程中的最优解对应的适应度：\n');
disp(sa_best_fitness);

% 绘制甘特图
[makespan, start_time] = schedule(best_sol, job_num, machine_num);
plot_gantt_chart(job_num, machine_num, start_time, makespan);

% 绘制收敛图
figure;
plot(1:max_gen, best_fitness_each_gen, 'r', 'LineWidth', 2);
hold on;
plot(1:max_gen, avg_fitness_each_gen, 'b', 'LineWidth', 2);
plot(1:max_gen, sa_best_fitness_each_step, 'g', 'LineWidth', 2);
legend('最优解', '平均适应度', '退火过程中的最优解');
xlabel('迭代次数');
ylabel('适应度');
title('收敛图');

% 选择函数
function selected = tournament_selection(pop, fitness, k)
    pop_size = size(pop, 1);
    selected = zeros(k, size(pop, 2));
    for i = 1:k
        idx = randperm(pop_size, 2);
        if fitness(idx(1)) < fitness(idx(2))
            selected(i, :) = pop(idx(1), :);
        else
            selected(i, :) = pop(idx(2), :);
        end
    end
end

% 交叉函数
function [c1, c2] = crossover(p1, p2)
    len = length(p1);
    idx = randperm(len, 2);
    start = min(idx);
    stop = max(idx);
    c1 = p1;
    c2 = p2;
    for i = start:stop
        j1 = find(c1 == p2(i), 1);
        j2 = find(c2 == p1(i), 1);
        c1([i j1]) = c1([j1 i]);
        c2([i j2]) = c2([j2 i]);
    end
end

% 变异函数
function c = mutation(p)
    len = length(p);
    idx = randperm(len, 2);
    start = min(idx);
    stop = max(idx);
    rev = fliplr(p(start:stop));
    c = [p(1:start-1) rev p(stop+1:end)];
end

% 精英选择函数
function [pop, fitness] = elitist_selection(pop, fitness, pop_size)
    [sorted_fitness, idx] = sort(fitness);
    pop = pop(idx, :);
    fitness = sorted_fitness;
    if size(pop, 1) > pop_size
        pop = pop(1:pop_size, :);
        fitness = fitness(1:pop_size);
    end
end

% 调度函数
function [makespan, start_time] = schedule(job_seq, job_num, machine_num)
    processing_time = rand(job_num, machine_num); % 生成随机处理时间
    start_time = zeros(job_num, machine_num); % 记录每道工序的开始时间
    end_time = zeros(job_num, machine_num); % 记录每道工序的结束时间
    for j = 1:machine_num
        if j == 1
            start_time(1, j) = 0;
        else
            start_time(1, j) = end_time(1, j-1);
        end
        end_time(1, j) = start_time(1, j) + processing_time(job_seq(1), j);
    end
    for i = 2:job_num
        for j = 1:machine_num
            if j == 1
                start_time(i, j) = end_time(i-1, j);
            else
                start_time(i, j) = max(end_time(i, j-1), end_time(i-1, j));
            end
            end_time(i, j) = start_time(i, j) + processing_time(job_seq(i), j);
        end
    end
    makespan = max(end_time(:, end));
end

% 绘制甘特图函数
function plot_gantt_chart(job_num, machine_num, start_time, makespan)
    color = lines(job_num);
    figure;
    for i = 1:job_num
        for j = 1:machine_num
            x = [start_time(i, j) start_time(i, j) start_time(i, j)+start_time(job_num, machine_num)];
            y = [i-0.4 i+0.4 i];
            patch(x, y, color(i, :));
        end
    end
    xlabel('时间');
    ylabel('工件');
    xlim([0 makespan]);
    ylim([0 job_num+1]);
    title('甘特图');
end