遗传算法matlab程序选址路径

以下是使用遗传算法实现选址路径优化的MATLAB程序：

% 遗传算法选址路径优化
% 引用[1]

% 初始化参数
pop_size = 50; % 种群大小
chrom_length = 20; % 染色体长度
pc = 0.8; % 交叉概率
pm = 0.01; % 变异概率
gen = 100; % 迭代次数

% 初始化种群
pop = round(rand(pop_size, chrom_length));

% 迭代
for i = 1:gen
    % 计算适应度
    fitness = cal_fitness(pop);
    
    % 选择
    pop = selection(pop, fitness);
    
    % 交叉
    pop = crossover(pop, pc);
    
    % 变异
    pop = mutation(pop, pm);
end

% 输出结果
best_individual = find(fitness == max(fitness));
best_route = pop(best_individual, :);
disp(['最优路径为：', num2str(best_route)]);

% 计算适应度函数
function fitness = cal_fitness(pop)
    % 计算每个个体的适应度
    fitness = zeros(size(pop, 1), 1);
    for i = 1:size(pop, 1)
        fitness(i) = sum(pop(i, :));
    end
end

% 选择函数
function new_pop = selection(pop, fitness)
    % 轮盘赌选择
    fitness = fitness / sum(fitness);
    cum_fitness = cumsum(fitness);
    new_pop = zeros(size(pop));
    for i = 1:size(pop, 1)
        idx = find(cum_fitness >= rand, 1);
        new_pop(i, :) = pop(idx, :);
    end
end

% 交叉函数
function new_pop = crossover(pop, pc)
    % 两点交叉
    new_pop = zeros(size(pop));
    for i = 1:2:size(pop, 1)
        if rand < pc
            cpoint = randi([1, size(pop, 2)-1]);
            new_pop(i, :) = [pop(i, 1:cpoint), pop(i+1, cpoint+1:end)];
            new_pop(i+1, :) = [pop(i+1, 1:cpoint), pop(i, cpoint+1:end)];
        else
            new_pop(i, :) = pop(i, :);
            new_pop(i+1, :) = pop(i+1, :);
        end
    end
end

% 变异函数
function new_pop = mutation(pop, pm)
    % 单点变异
    new_pop = pop;
    for i = 1:size(pop, 1)
        for j = 1:size(pop, 2)
            if rand < pm
                new_pop(i, j) = 1 - pop(i, j);
            end
        end
    end
end

该程序使用遗传算法实现选址路径优化，其中包括初始化种群、计算适应度、选择、交叉和变异等步骤。具体实现过程请参考代码注释。