114 lines
4.1 KiB
Python
114 lines
4.1 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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def main():
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# 初始化数据
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order_data = OrderData()
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risk_data = RiskEnterpriseData()
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supplier_data = SupplierData()
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config = Config()
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# 初始化工具类
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utils = ChromosomeUtils(order_data, risk_data, supplier_data)
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calculator = ObjectiveCalculator(order_data, risk_data, supplier_data, utils, config)
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encoder = Encoder(config, utils)
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genetic_op = GeneticOperator(config, utils)
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nsga2 = NSGA2(config.pop_size, config.objective_num)
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visualizer = ResultVisualizer(utils)
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# 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: {population.shape}")
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# 记录历史
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all_objectives = []
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convergence_history = []
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best_front = []
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best_front_objs = []
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no_improve_count = 0
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prev_best_avg = float('inf')
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# 进化过程
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for generation in range(config.max_generations):
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# 计算目标函数
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objectives = [calculator.calculate_objectives(chrom) for chrom in population]
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all_objectives.extend(objectives)
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# 记录当前代的最优前沿
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ranks, fronts = nsga2.fast_non_dominated_sort(objectives)
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current_front = fronts[0]
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current_front_objs = [objectives[i] for i in current_front]
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best_front = population[current_front]
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best_front_objs = current_front_objs
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# 收敛判断（基于前沿平均目标值）
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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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current_avg = avg_cost + avg_tardiness
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if abs(current_avg - prev_best_avg) < 1e-4:
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no_improve_count += 1
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else:
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no_improve_count = 0
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prev_best_avg = current_avg
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else:
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no_improve_count += 1
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# 选择（锦标赛选择）
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selected = nsga2.selection(population, objectives)
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# 交叉
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offspring = []
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i = 0
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while len(offspring) < config.pop_size:
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if i+1 < config.pop_size 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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offspring.append(child1)
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if len(offspring) < config.pop_size:
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offspring.append(child2)
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else:
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offspring.append(selected[i])
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i += 2
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# 变异
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offspring = [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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offspring = np.array(offspring[:config.pop_size])
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# 合并父代和子代
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combined = np.vstack([population, offspring])
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combined_objs = objectives + [calculator.calculate_objectives(chrom) for chrom in offspring]
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# 环境选择（核心：NSGA-II的环境选择）
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population, objectives = nsga2.environmental_selection(combined, combined_objs)
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# 早停检查
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if no_improve_count >= config.early_stop_patience:
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print(f"早停于第 {generation} 代")
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break
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if generation % 50 == 0:
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print(f"第 {generation} 代完成，当前最优前沿大小: {len(current_front)}")
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# 结果可视化与打印
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print("进化完成，绘制结果...")
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visualizer.plot_pareto_front(all_objectives, best_front_objs)
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visualizer.plot_convergence(convergence_history)
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visualizer.print_pareto_solutions(best_front, best_front_objs)
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if __name__ == "__main__":
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random.seed(42)
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np.random.seed(42)
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main() |