217 lines
7.3 KiB
Python
217 lines
7.3 KiB
Python
import csv
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import json
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import os
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import random
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from datetime import datetime
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from deap import tools
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from tqdm import tqdm
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import matplotlib.pyplot as plt
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from GA_Agent_0925.creating import creating
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from controller_db import ControllerDB
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from evaluate_func import fitness
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# 遗传算法主函数（单进程）
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def main():
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# 1️⃣ 加载配置
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with open("config.json", "r", encoding="utf-8") as f:
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cfg = json.load(f)
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random.seed(cfg["seed"])
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print("\n📘 参数配置:")
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for k, v in cfg.items():
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print(f" {k}: {v}")
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print("-" * 40)
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# ==============================
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# 2️⃣ 初始化数据库控制器
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# ==============================
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controller_db_obj = ControllerDB("without_exp", reset_flag=0)
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controller_db_obj.reset_db(force_drop=True)
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controller_db_obj.prepare_list_sample()
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# ==============================
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# 3️⃣ 初始化 GA
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# ==============================
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toolbox = creating()
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pop = toolbox.population(n=cfg["pop_size"])
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hof = tools.HallOfFame(1)
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stats = tools.Statistics(lambda ind: ind.fitness.values)
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stats.register("max", lambda fits: max(f[0] for f in fits))
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best_list = []
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# ==============================
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# 4️⃣ 结果保存准备
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# ==============================
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results_dir = "results"
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os.makedirs(results_dir, exist_ok=True)
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txt_result_file = os.path.join(results_dir, "best_individual_each_gen.txt")
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json_result_file = os.path.join(results_dir, "best_result_with_industry.json")
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csv_file = "convergence0119_data.csv"
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exp_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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# TXT 初始化
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with open(txt_result_file, "a", encoding="utf-8") as f:
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f.write(f"\n实验开始时间：{exp_time}\n")
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f.write("每一代最优个体基因参数如下：\n")
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# CSV 表头
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write_header = not os.path.exists(csv_file)
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if write_header:
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with open(csv_file, "w", newline="", encoding="utf-8") as f:
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writer = csv.writer(f)
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writer.writerow([
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"Timestamp",
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"Generation",
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"Best_Fitness_Percentage"
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])
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# ==============================
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# 5️⃣ Fitness 缓存（关键加速）
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# ==============================
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fitness_cache = {}
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# ==============================
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# 6️⃣ 主进化循环
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# ==============================
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for gen in tqdm(range(cfg["n_gen"]), desc="进化中", ncols=90):
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# ---------- 评估当前种群 ----------
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invalid_ind = [ind for ind in pop if not ind.fitness.valid]
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for ind in invalid_ind:
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gene_key = tuple(round(x, 4) for x in ind)
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if gene_key in fitness_cache:
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ind.fitness.values = fitness_cache[gene_key]
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else:
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controller_db_obj.reset_sample_db()
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controller_db_obj.prepare_list_sample()
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fit_val = fitness(ind, controller_db_obj=controller_db_obj)
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ind.fitness.values = fit_val
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fitness_cache[gene_key] = fit_val
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# ---------- 选择 ----------
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offspring = toolbox.select(pop, len(pop))
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offspring = list(map(toolbox.clone, offspring))
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# ---------- 交叉 ----------
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for child1, child2 in zip(offspring[::2], offspring[1::2]):
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if random.random() < cfg["cx_prob"]:
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toolbox.mate(child1, child2)
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del child1.fitness.values, child2.fitness.values
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# ---------- 变异 ----------
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for mutant in offspring:
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if random.random() < cfg["mut_prob"]:
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toolbox.mutate(mutant)
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del mutant.fitness.values
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# ---------- 评估新个体 ----------
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invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
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for ind in invalid_ind:
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gene_key = tuple(round(x, 4) for x in ind)
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if gene_key in fitness_cache:
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ind.fitness.values = fitness_cache[gene_key]
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else:
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controller_db_obj.reset_sample_db()
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controller_db_obj.prepare_list_sample()
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fit_val = fitness(ind, controller_db_obj=controller_db_obj)
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ind.fitness.values = fit_val
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fitness_cache[gene_key] = fit_val
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pop[:] = offspring
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# ---------- 精英保留（Elitism） ----------
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elite = hof[0] if len(hof) > 0 else None
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if elite is not None:
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# 替换当前种群中最差的个体（假设适应度越大越差）
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worst_idx = max(range(len(pop)), key=lambda i: pop[i].fitness.values[0])
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pop[worst_idx] = toolbox.clone(elite)
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# 更新 hof
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hof.update(pop)
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# ---------- 记录最优 ----------
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best_ind = tools.selBest(pop, 1)[0]
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best_fitness = best_ind.fitness.values[0] # error，越小越好
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best_gene = list(map(float, best_ind))
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best_ga_id = getattr(best_ind, "ga_id", None)
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# ---------- 维护“历史最优” ----------
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if 'prev_best_fitness' not in globals():
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prev_best_fitness = best_fitness
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prev_best_gene = best_gene
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prev_best_ga_id = best_ga_id
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else:
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if best_fitness < prev_best_fitness: # 更优（误差更小）
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prev_best_fitness = best_fitness
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prev_best_gene = best_gene
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prev_best_ga_id = best_ga_id
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# 否则：保持历史最优不变
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with open(txt_result_file, "a", encoding="utf-8") as f:
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f.write(
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f"第 {gen + 1} 代 | 最优适应度: {prev_best_fitness:.4f} | 基因: {prev_best_gene}\n"
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)
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# CSV
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with open(csv_file, "a", newline="", encoding="utf-8") as f:
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writer = csv.writer(f)
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writer.writerow([
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exp_time,
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gen,
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best_fitness
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])
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# ---------- 定期清理 DB ----------
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if gen % 3 == 0:
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controller_db_obj.drop_table(
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"without_exp_result",
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keep_ga_id=prev_best_ga_id
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)
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# ==============================
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# 7️⃣ 输出最终结果
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# ==============================
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print("\n✅ 进化完成！")
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print(f"🏆 最优个体: {list(map(float, hof[0]))}")
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print(f"🌟 最优适应度: {hof[0].fitness.values[0]:.4f}")
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best_list.append(prev_best_fitness)
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# ==============================
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# 8️⃣ 绘制收敛曲线（仅 Best）
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# ==============================
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plt.figure(figsize=(12, 8))
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plt.plot(best_list, linewidth=2, label="Best Fitness")
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plt.xlabel("Generation")
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plt.ylabel("Error")
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plt.legend()
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plt.grid(True, alpha=0.3)
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plt.tight_layout()
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plt.savefig("convergence0119.png", dpi=300)
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plt.close()
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# ==============================
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# 9️⃣ 保存 JSON 结果
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# ==============================
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result_data = {
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"config": cfg,
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"best_individual": list(map(float, hof[0])),
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"best_fitness": float(hof[0].fitness.values[0]),
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"fitness_curve": {
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"best_list": best_list
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},
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"timestamp": exp_time
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}
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with open(json_result_file, "w", encoding="utf-8") as f:
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json.dump(result_data, f, indent=4, ensure_ascii=False)
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print(f"\n💾 最优结果已保存至: {json_result_file}")
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if __name__ == "__main__":
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main()
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