235 lines
13 KiB
Python
235 lines
13 KiB
Python
import random
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import numpy as np
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from data_structures import OrderData, RiskEnterpriseData, SupplierData, Config
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from chromosome_utils import ChromosomeUtils
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from objective_calculator import ObjectiveCalculator
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from encoder import Encoder
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from genetic_operators import GeneticOperator
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from nsga2 import NSGA2
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from visualizer import ResultVisualizer
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from data_structures import DataStructures
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def main():
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"""主函数：执行NSGA-II算法求解多目标优化问题"""
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try:
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# 1. 初始化随机种子（确保结果可复现）
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random.seed(42)
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np.random.seed(42)
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# 2. 初始化数据（订单、风险企业、供应商、算法配置）
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print("初始化数据结构...")
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order_data = OrderData() # 订单数据（需求、交货期等，整数）
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risk_data = RiskEnterpriseData() # 风险企业数据
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supplier_data = SupplierData() # 供应商数据
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config = Config() # 算法参数配置
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# 3. 初始化工具类和算法组件
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print("初始化算法组件...")
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utils = ChromosomeUtils(order_data, risk_data, supplier_data) # 染色体工具
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calculator = ObjectiveCalculator(order_data, risk_data, supplier_data, utils, config) # 目标函数计算器
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encoder = Encoder(config, utils) # 种群初始化编码器
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genetic_op = GeneticOperator(config, utils) # 遗传操作器
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nsga2 = NSGA2(config.pop_size, config.objective_num) # NSGA-II算法实例
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visualizer = ResultVisualizer(utils) # 结果可视化工具
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# 4. 初始化种群
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print("初始化种群...")
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population = encoder.initialize_population()
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print(f"初始化种群完成，（种群大小，染色体长度）: {population.shape if population.size > 0 else '空'}")
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# 若种群初始化失败（为空），直接退出
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if population.size == 0:
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print("错误：种群初始化失败，无法继续进化")
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return
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# 5. 记录进化过程中的历史数据
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all_objectives = [] # 所有代的目标函数值
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convergence_history = [] # 收敛趋势（每代最优前沿的平均目标值，整数）
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best_front = [] # 最终帕累托前沿解
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best_front_objs = [] # 最终帕累托前沿的目标值
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prev_avg_cost = None # 上一代的平均成本
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prev_avg_tardiness = None # 上一代的平均延期
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no_improve_count = 0 # 无改进计数器
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# 6. 进化主循环
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print(f"开始进化（最大代数：{config.max_generations}，早停耐心：{config.early_stop_patience}）...")
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for generation in range(config.max_generations):
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try:
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# 计算当前种群的目标函数值
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objectives = [calculator.calculate_objectives(chrom) for chrom in population]
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all_objectives.extend(objectives) # 记录所有目标值
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# 非支配排序，获取当前代的帕累托前沿
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ranks, fronts = nsga2.fast_non_dominated_sort(objectives)
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current_front = fronts[0] if fronts else [] # 第0层为最优前沿
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current_front_objs = [objectives[i] for i in current_front] if current_front else []
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best_front = population[current_front] if current_front else [] # 更新当前最优前沿解
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best_front_objs = current_front_objs # 更新当前最优前沿目标值
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# 记录收敛趋势（基于最优前沿的平均目标值，整数）
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# 记录收敛趋势并判断早停条件
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if len(current_front_objs) > 0:
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avg_cost = sum(obj[0] for obj in current_front_objs) // len(current_front_objs)
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avg_tardiness = sum(obj[1] for obj in current_front_objs) // len(current_front_objs)
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convergence_history.append((avg_cost, avg_tardiness))
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# 早停判断逻辑
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if prev_avg_cost is not None and prev_avg_tardiness is not None:
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# 计算两个目标的变化量
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cost_change = abs(avg_cost - prev_avg_cost)
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tardiness_change = abs(avg_tardiness - prev_avg_tardiness)
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# 检查是否两个目标的变化率
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if (cost_change < config.early_stop_threshold * prev_avg_cost and
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tardiness_change < config.early_stop_threshold * prev_avg_tardiness):
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no_improve_count += 1
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else:
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no_improve_count = 0 # 有改进，重置计数器
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prev_avg_cost = avg_cost
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prev_avg_tardiness = avg_tardiness
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else:
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# 初始化历史值（第一代）
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prev_avg_cost = avg_cost
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prev_avg_tardiness = avg_tardiness
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no_improve_count = 0
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else:
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no_improve_count += 1 # 无前沿解，视为无改进
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# 早停检查（连续多代无改进则停止）
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if no_improve_count >= config.early_stop_patience:
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print(
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f"早停触发：连续{no_improve_count}代两个目标值变化率均小于{config.early_stop_threshold}，终止于第{generation}代")
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break
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# 选择操作（锦标赛选择）
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selected = nsga2.selection(population, objectives)
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# 校验选择后的种群大小
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assert len(selected) == config.pop_size, \
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f"选择后的种群大小({len(selected)})与目标大小({config.pop_size})不符"
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# 交叉操作（两点交叉）- 修复索引越界问题
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offspring = [] # 子代种群
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selected_len = len(selected) # selected的长度（等于pop_size）
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i = 0
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max_iter = 2 * config.pop_size # 最大迭代次数，避免无限循环
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iter_count = 0
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while len(offspring) < config.pop_size and iter_count < max_iter:
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iter_count += 1
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# 检查i是否超出selected范围，若超出则重置（循环使用个体）
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if i >= selected_len:
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i = 0
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# 尝试两两交叉（需i+1在范围内，且满足交叉概率）
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if i + 1 < selected_len and random.random() < config.crossover_prob:
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parent1 = selected[i]
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parent2 = selected[i + 1]
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child1, child2 = genetic_op.two_point_crossover(parent1, parent2)
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# 添加child1（若未达到种群大小）
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if len(offspring) < config.pop_size:
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offspring.append(child1)
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# 添加child2（若未达到种群大小）
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if len(offspring) < config.pop_size:
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offspring.append(child2)
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i += 2 # 处理下一对个体
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else:
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# 直接添加当前父代（避免i+1越界）
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offspring.append(selected[i])
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i += 1 # 处理下一个个体（步长改为1，避免快速越界）
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# 若迭代次数用尽仍未生成足够子代，补充随机个体（健壮性处理，整数）
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while len(offspring) < config.pop_size:
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offspring.append(encoder._generate_random_chromosome()) # 整数化随机染色体
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# 变异操作（均匀变异，整数化）
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offspring = [
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genetic_op.uniform_mutation(chrom) if random.random() < config.mutation_prob else chrom
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for chrom in offspring
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]
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offspring = np.array(offspring[:config.pop_size]).astype(int) # 确保子代大小和整数类型
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# 合并父代和子代，准备环境选择
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combined = np.vstack([population, offspring]).astype(int) # 合并种群
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# 计算合并种群的目标函数值
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combined_objs = objectives + [calculator.calculate_objectives(chrom) for chrom in offspring]
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# 环境选择（保留最优的pop_size个个体）
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population, objectives = nsga2.environmental_selection(combined, combined_objs)
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# 校验环境选择后的种群大小和整数类型
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population = population.astype(int)
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assert len(population) == config.pop_size, \
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f"环境选择后的种群大小({len(population)})与目标大小({config.pop_size})不符"
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# 早停检查（连续多代无改进则停止）
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if no_improve_count >= config.early_stop_patience:
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print(f"早停触发：连续{no_improve_count}代无改进，终止于第{generation}代")
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break
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# 每50代打印一次进度
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if generation % 50 == 0:
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front_size = len(current_front) if current_front else 0
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print(f"第{generation}代完成，当前最优前沿大小: {front_size}，无改进计数: {no_improve_count}")
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except Exception as e:
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print(f"第{generation}代进化出错：{str(e)}，跳过当前代")
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continue
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# 7. 结果可视化与输出
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print("进化完成，处理结果...")
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if len(best_front_objs) > 0:
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# 1. 过滤重复解（关键改进：基于目标值去重，确保相同目标值只保留一个解）
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unique_front = []
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unique_front_objs = []
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seen_obj = set() # 仅跟踪目标值，确保相同目标值只保留一个
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for sol, obj in zip(best_front, best_front_objs):
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obj_tuple = tuple(obj) # 将目标值转为元组用于查重
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if obj_tuple not in seen_obj:
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seen_obj.add(obj_tuple)
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unique_front.append(sol)
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unique_front_objs.append(obj)
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# 2. 计算评价指标并排序
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if unique_front_objs:
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# 找出各目标的最优值
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optimal_cost = min(obj[0] for obj in unique_front_objs)
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optimal_tardiness = min(obj[1] for obj in unique_front_objs)
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max_cost = max(obj[0] for obj in unique_front_objs)
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max_tardiness = max(obj[1] for obj in unique_front_objs)
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# 计算每个解的评价指标
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evaluated_solutions = []
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for sol, obj in zip(unique_front, unique_front_objs):
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index = DataStructures.calculate_evaluation_index(
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obj, optimal_cost, optimal_tardiness, max_cost, max_tardiness
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)
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evaluated_solutions.append((sol, obj, index))
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# 按评价指标升序排序
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evaluated_solutions.sort(key=lambda x: x[2])
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# 选择前n个解
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top_n = min(config.print_top_n, len(evaluated_solutions))
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top_solutions = evaluated_solutions[:top_n]
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top_population = [sol for sol, _, _ in top_solutions]
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top_objectives = [obj for _, obj, _ in top_solutions]
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# 打印排序后的解的评价指标
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print("\n" + "=" * 100)
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print(f"排序后的前{top_n}个最优解（评价指标从小到大）")
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print("=" * 100)
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for i, (_, obj, idx) in enumerate(top_solutions):
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print(f"解 {i + 1}: 成本={obj[0]}, 延期={obj[1]}, 评价指标={idx:.4f}")
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else:
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top_population = []
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top_objectives = []
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# 保持原有图表绘制逻辑不变
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visualizer.plot_pareto_front(all_objectives, best_front_objs) # 绘制帕累托前沿
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visualizer.plot_convergence(convergence_history) # 绘制收敛趋势
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# 打印处理后的最优解详情
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if top_population:
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visualizer.print_pareto_solutions(top_population, top_objectives)
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else:
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print("未找到有效帕累托前沿解")
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else:
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print("未找到有效帕累托前沿解")
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except Exception as e:
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print(f"程序运行出错：{str(e)}")
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import traceback
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traceback.print_exc() # 打印详细错误栈
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if __name__ == "__main__":
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print("程序启动...")
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main()
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print("程序结束") |