用matlab多种群遗传算法代码

以下是一个使用MATLAB实现多种群遗传算法的示例代码：

% 参数设置
num_populations = 5; % 种群数量
population_size = 50; % 种群大小
num_generations = 100; % 迭代次数

% 初始化种群
populations = cell(num_populations, 1);
for i = 1:num_populations
    populations{i} = rand(population_size, num_variables); % num_variables为变量数量
end

% 迭代优化
best_fitness = zeros(num_generations, 1);
for gen = 1:num_generations
    % 计算适应度值
    fitness_values = zeros(num_populations, population_size);
    for i = 1:num_populations
        for j = 1:population_size
            fitness_values(i, j) = fitness_function(populations{i}(j, :));
        end
    end
    
    % 选择操作
    new_populations = cell(num_populations, 1);
    for i = 1:num_populations
        % 锦标赛选择方法，随机选择若干个个体进行比较，选出适应度最好的个体
        tournament_size = 5;
        selected_indices = zeros(population_size, 1);
        for j = 1:population_size
            tournament_indices = randperm(population_size, tournament_size);
            [~, idx] = max(fitness_values(i, tournament_indices));
            selected_indices(j) = tournament_indices(idx);
        end
        new_populations{i} = populations{i}(selected_indices, :);
    end
    
    % 交叉操作
    crossover_probability = 0.8;
    for i = 1:num_populations
        for j = 1:population_size/2
            if rand < crossover_probability
                parent_indices = randperm(num_populations, 2);
                parent1 = new_populations{parent_indices(1)}(j, :);
                parent2 = new_populations{parent_indices(2)}(j, :);
                
                % 交叉方法，这里使用单点交叉
                crossover_point = randi(num_variables);
                child1 = [parent1(1:crossover_point), parent2(crossover_point+1:end)];
                child2 = [parent2(1:crossover_point), parent1(crossover_point+1:end)];
                
                new_populations{parent_indices(1)}(j, :) = child1;
                new_populations{parent_indices(2)}(j, :) = child2;
            end
        end
    end
    
    % 变异操作
    mutation_probability = 0.05;
    for i = 1:num_populations
        for j = 1:population_size
            if rand < mutation_probability
                % 变异方法，这里使用单点变异
                mutation_point = randi(num_variables);
                new_populations{i}(j, mutation_point) = rand;
            end
        end
    end
    
    % 更新种群
    populations = new_populations;
    
    % 记录每代最佳适应度值
    best_fitness(gen) = max(max(fitness_values));
end

% 绘制适应度曲线
plot(best_fitness);
xlabel('Generation');
ylabel('Best Fitness');
title('Genetic Algorithm');

% 适应度函数示例
function fitness = fitness_function(variables)
    % 根据实际问题定义适应度函数
    % 这里仅为示例，假设适应度为变量之和
    fitness = sum(variables);
end

请注意，这只是一个简单的示例代码，用于说明多种群遗传算法的基本实现。在实际应用中，你需要根据具体问题设计适应度函数和选择、交叉、变异等操作的方式。