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%% 主函数部分
% function main()clear; clc; close all;% 生成或加载算例
filename = 'D:\快递优化\LogisticsInstance.mat';
if ~exist(filename, 'file')instance = generate_instance();save(filename, '-struct', 'instance');
elseinstance = load(filename);
end% 遗传算法参数配置
ga_config = struct(...'pop_size', 10, ...    % 种群大小'max_gen', 100, ...     % 最大迭代次数'px', 0.9, ...         % 交叉概率'pm', 0.1 ...          % 变异概率);% 运行四种策略
strategies = {'Strategy1', 'Strategy2', 'Strategy3', 'Strategy4'};
results = cell(4,1);for s = 1:4fprintf('正在运行 %s...\n', strategies{s});results{s} = ga_optimizer(instance, ga_config, s);
end% 结果可视化
visualize_results(results, strategies);%% ========== 生成算例功能函数 ==========
function instance = generate_instance()
rng(2023); % 固定随机种子保证可重复性% 基本参数设置
n_orders = 1000;          % 订单总数
address_repeat_rate = 0.02; % 地址重复率
vehicle_capacity = 800;   % 车辆载重限制
unit_cost = 0.3;          % 快递单位成本% 生成订单地址（2%重复率）
unique_addrs = ceil(n_orders*(1-address_repeat_rate));
addresses = [1:unique_addrs, randi(unique_addrs, 1, n_orders-unique_addrs)];
addresses = addresses(randperm(n_orders));% 生成订单重量（50%在[1,15]，50%在[16,20]）
weights = [randi([1 15], 1, floor(n_orders/2)), randi([16 20], 1, ceil(n_orders/2))];
weights = weights(randperm(n_orders));% 生成配送时间矩阵
time_matrix = rand(n_orders) * 10; % 基础时间矩阵
time_matrix = triu(time_matrix) + triu(time_matrix)'; % 对称化
time_matrix(1:n_orders+1:end) = 0; % 对角线置零% 处理地址重复的情况
[~, ~, addr_groups] = unique(addresses);
for k = 1:max(addr_groups)idx = find(addr_groups == k);if numel(idx) > 1time_matrix(idx, idx) = 0;end
end% 时段相关参数
tariffs = [0.7, 0.8, 1.0, 0.5, 0.3]; % 五个时段的关税
route_costs = [1.0, 0.1, 0.3, 0.5, 0.7]; % 路线成本
unit_prices = 0.5 + 0.3*rand(1, n_orders); % 雇佣单价% 打包实例数据
instance = struct(...'n_orders', n_orders, ...'addresses', addresses, ...'weights', weights, ...'time_matrix', time_matrix, ...'tariffs', tariffs, ...'route_costs', route_costs, ...'unit_prices', unit_prices, ...'vehicle_capacity', vehicle_capacity, ...'unit_cost', unit_cost ...);
end%% ========== 具体算法功能函数 ==========
% 遗传算法优化器
function result = ga_optimizer(instance, config, strategy_id)
% 初始化种群
pop = randi([1 6], config.pop_size, instance.n_orders);best_f1 = inf;
best_chrom = [];
history = zeros(config.max_gen, 2);for gen = 1:config.max_gen% 评估适应度fitness = zeros(config.pop_size