装配线平衡遗传算算法matlab源码

以下是一个基本的装配线平衡问题的遗传算法 MATLAB 源代码。请注意，这只是一个基本的实现，您可能需要根据您的具体问题进行调整和修改。

% 装配线平衡问题的遗传算法 MATLAB 源代码

% 定义问题参数
num_stations = 6;  % 站点数
station_time = [4 2 5 3 1 6];  % 每个站点的处理时间
switch_time = [0 1 2 1 2 0];  % 切换站点所需的时间
num_individuals = 50;  % 种群大小
mutation_rate = 0.05;  % 变异率
crossover_rate = 0.8;  % 交叉率
num_generations = 100;  % 迭代次数

% 初始化种群
population = zeros(num_individuals, num_stations);
for i = 1:num_individuals
    population(i, :) = randperm(num_stations);
end

% 迭代
for g = 1:num_generations
    % 计算适应度
    fitness = zeros(num_individuals, 1);
    for i = 1:num_individuals
        fitness(i) = calculate_fitness(population(i, :), station_time, switch_time);
    end
    
    % 选择
    parents = tournament_selection(population, fitness);
    
    % 交叉
    offspring = crossover(parents, crossover_rate);
    
    % 变异
    offspring = mutation(offspring, mutation_rate);
    
    % 合并种群并选择顶部个体
    combined_population = [population; offspring];
    combined_fitness = zeros(num_individuals*2, 1);
    for i = 1:num_individuals*2
        combined_fitness(i) = calculate_fitness(combined_population(i, :), station_time, switch_time);
    end
    [sorted_fitness, sorted_indices] = sort(combined_fitness, 'descend');
    top_individuals = combined_population(sorted_indices(1:num_individuals), :);
    
    % 更新种群
    population = top_individuals;
end

% 打印最佳个体
best_individual = population(1, :);
best_fitness = calculate_fitness(best_individual, station_time, switch_time);
disp(['最佳个体: ' num2str(best_individual)]);
disp(['适应度: ' num2str(best_fitness)]);

% 计算适应度的函数
function fitness = calculate_fitness(individual, station_time, switch_time)
    num_stations = length(individual);
    time = zeros(num_stations, 1);
    time(1) = station_time(individual(1));
    for i = 2:num_stations
        time(i) = time(i-1) + switch_time(individual(i-1)) + station_time(individual(i));
    end
    fitness = 1 / max(time);
end

% 锦标赛选择函数
function parents = tournament_selection(population, fitness)
    num_individuals = size(population, 1);
    num_parents = size(population, 1) / 2;
    parents = zeros(num_parents, size(population, 2));
    for i = 1:num_parents
        tournament_indices = randperm(num_individuals, 2);
        tournament_fitness = fitness(tournament_indices);
        [~, winner_index] = max(tournament_fitness);
        winner_individual = population(tournament_indices(winner_index), :);
        parents(i, :) = winner_individual;
    end
end

% 交叉函数
function offspring = crossover(parents, crossover_rate)
    num_parents = size(parents, 1);
    num_genes = size(parents, 2);
    offspring = zeros(num_parents, num_genes);
    for i = 1:num_parents
        if rand() < crossover_rate
            partner_index = randperm(num_parents, 1);
            parent_1 = parents(i, :);
            parent_2 = parents(partner_index, :);
            crossover_point = randi(num_genes-1);
            offspring(i, 1:crossover_point) = parent_1(1:crossover_point);
            offspring(i, crossover_point+1:end) = parent_2(crossover_point+1:end);
        else
            offspring(i, :) = parents(i, :);
        end
    end
end

% 变异函数
function mutated_offspring = mutation(offspring, mutation_rate)
    num_offspring = size(offspring, 1);
    num_genes = size(offspring, 2);
    mutated_offspring = zeros(num_offspring, num_genes);
    for i = 1:num_offspring
        if rand() < mutation_rate
            mutation_indices = randperm(num_genes, 2);
            mutated_offspring(i, mutation_indices) = offspring(i, mutation_indices([2 1]));
        else
            mutated_offspring(i, :) = offspring(i, :);
        end
    end
end

该代码使用经典的遗传算法实现装配线平衡问题的优化。它定义了问题参数，如处理时间、切换时间、种群大小、变异率等。然后，它初始化种群，并在每个迭代中计算适应度、选择、交叉和变异。最后，它选择最佳个体并输出结果。请注意，这只是一个基本的实现，您可能需要根据您的具体问题进行调整和修改。