no message

GA_Agent_0925/main.py

@@ -1,5 +1,8 @@
 import json
+import os
 import random
+from datetime import datetime
+
 from deap import tools
 from sqlalchemy.orm import close_all_sessions
 from tqdm import tqdm
@@ -28,7 +31,7 @@ def main():
 
     # 2️⃣ 初始化 ControllerDB（数据库连接）
     controller_db_obj = ControllerDB("without_exp", reset_flag=0)
-    controller_db_obj.reset_db(force_drop=False)
+    controller_db_obj.reset_db(force_drop=True)
     # 准备样本表
     controller_db_obj.prepare_list_sample()
     # 2️⃣ 初始化工具箱
@@ -42,6 +45,22 @@ def main():
     best_list = []
     avg_list = []
 
+    # ============================================================
+    # 🔧 新增内容 1：准备保存每代最优个体的文件
+    # ============================================================
+    results_dir = "results"
+    os.makedirs(results_dir, exist_ok=True)
+
+    # 文件名
+    txt_result_file = os.path.join(results_dir, "best_individual_each_gen.txt")
+    json_result_file = os.path.join(results_dir, "best_result_with_industry.json")
+
+    # 写入第一行：实验时间（年月日+小时）
+    with open(txt_result_file, "w", encoding="utf-8") as f:
+        exp_time = datetime.now().strftime("%Y-%m-%d %H")
+        f.write(f"实验开始时间（年月日-小时）：{exp_time}\n\n")
+        f.write("以下为每一代的最优个体基因参数：\n")
+
     # ==============================
     # 主进化循环
     # ==============================
@@ -81,6 +100,29 @@ def main():
         best_list.append(record["max"])
         avg_list.append(record["avg"])
 
+        # ============================================================
+        # 🔧 新增内容 2：每代实时记录最优基因到文件
+        # ============================================================
+        best_ind = tools.selBest(pop, 1)[0]
+        best_gene = list(map(float, best_ind))
+        best_ga_id = getattr(best_ind, "ga_id", None)  # 获取 ga_id，如果没有就返回 None
+
+        # 写入 TXT 文件
+        with open(txt_result_file, "a", encoding="utf-8") as f:
+            f.write(
+                (f"第 {gen + 1} 代最优基因：{best_gene}   最优适应度: {best_ind.fitness.values[0]:.4f}"
+                 if best_gene else "N/A")
+                + "\n"
+            )
+
+        # ============================================================
+        # 新增：删除上一轮产生的临时表
+        # ============================================================
+
+        # 保留当前代最优 ga_id：
+        controller_db_obj.drop_table("without_exp_result", keep_ga_id=best_ga_id)
+        # 希望彻底删除整张表：
+        # controller_db_obj.drop_table("without_exp_result")
     # ==============================
     # 输出最优结果
     # ==============================
@@ -89,7 +131,7 @@ def main():
     print(f"🌟 最优适应度: {hof[0].fitness.values[0]:.4f}")
 
     # 绘制收敛曲线
-    plt.figure(figsize=(8, 5))
+    plt.figure(figsize=(12, 12))
     plt.plot(best_list, label="Best Fitness", linewidth=2)
     plt.plot(avg_list, label="Average Fitness", linestyle="--")
     plt.title("Genetic Algorithm Convergence")
@@ -98,6 +140,7 @@ def main():
     plt.legend()
     plt.grid(True, alpha=0.3)
     plt.tight_layout()
+    plt.savefig("convergence1.png", dpi=300)
     plt.show()
 
     # ==============================
@@ -105,5 +148,31 @@ def main():
     # ==============================
     print("\n📊 计算最优个体产业匹配情况...")
 
+    # ==============================
+    # 保存结果到文件
+    # ==============================
+
+    results_dir = "results"
+    os.makedirs(results_dir, exist_ok=True)
+
+    # 固定保存文件名
+    result_file = os.path.join(results_dir, "best_result_with_industry.json")
+
+    result_data = {
+        "config": cfg,
+        "best_individual": list(map(float, hof[0])),
+        "best_fitness": float(hof[0].fitness.values[0]),
+        "fitness_curve": {
+            "best_list": best_list,
+            "avg_list": avg_list
+        },
+        "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    }
+
+    with open(result_file, "w", encoding="utf-8") as f:
+        json.dump(result_data, f, indent=4, ensure_ascii=False)
+
+    print(f"\n💾 最优结果已保存至: {result_file}")
+
 if __name__ == "__main__":
     main()