diff --git a/data_structures.py b/data_structures.py
index a8dd21e..d7e8bac 100644
--- a/data_structures.py
+++ b/data_structures.py
@@ -71,7 +71,7 @@ class Config:
     """算法参数配置类：存储NSGA-II的各类参数"""
     def __init__(self):
         # 种群参数
-        self.pop_size = 300  # 种群大小
+        self.pop_size = 100  # 种群大小
         self.N1_ratio = 0.2  # 优先成本的种群比例
         self.N2_ratio = 0.2  # 优先延期的种群比例
         self.N3_ratio = 0.3  # 强制风险企业的种群比例
@@ -83,11 +83,12 @@ class Config:
         # 惩罚系数
         self.delta = 1.3  # 变更惩罚系数
         # 早停参数
-        self.early_stop_patience = 50  # 连续多少代无改进则早停
+        self.early_stop_patience = 20  # 连续多少代无改进则早停
+        self.early_stop_threshold = 0.15  # 目标值变化阈值
         # 目标函数数量
         self.objective_num = 2  # 双目标（成本+延期）
-        self.duplicate_threshold = 0.1  # 重复解保留数量
-        self.print_top_n = 5  # 打印前N个最优解
+        self.duplicate_threshold = 0.05  # 重复解保留数量
+        self.print_top_n = 10  # 打印前N个最优解
 
 class DataStructures:
     """数据结构工具类：提供评价指标计算等功能"""
@@ -103,9 +104,9 @@ class DataStructures:
         cost, tardiness = objectives
         # 避免除以零（成本最优值为0时的保护）
         if optimal_cost == 0:
-            cost_ratio = 0 if cost == 0 else float('inf')
+            cost_ratio = cost
         else:
-            cost_ratio = cost / optimal_cost
+            cost_ratio = 2*(cost / optimal_cost)
         # 延期处理（+1避免除以零）
-        tardiness_ratio = (tardiness + 1) / (optimal_tardiness + 1)
+        tardiness_ratio = 8*((tardiness + 1) / (optimal_tardiness + 1))
         return cost_ratio + tardiness_ratio
\ No newline at end of file
diff --git a/main.py b/main.py
index 4266a39..768d871 100644
--- a/main.py
+++ b/main.py
@@ -44,7 +44,9 @@ def main():
         best_front = []  # 最终帕累托前沿解（整数）
         best_front_objs = []  # 最终帕累托前沿的目标值（整数）
         no_improve_count = 0  # 无改进计数器（用于早停）
-        prev_best_avg = float('inf')  # 上一代的最优平均目标值（整数求和）
+        prev_avg_cost = None  # 上一代的平均成本
+        prev_avg_tardiness = None  # 上一代的平均延期
+        no_improve_count = 0  # 无改进计数器
         # 6. 进化主循环
         print(f"开始进化（最大代数：{config.max_generations}，早停耐心：{config.early_stop_patience}）...")
         for generation in range(config.max_generations):
@@ -60,20 +62,39 @@ def main():
                 best_front = population[current_front] if current_front else []  # 更新当前最优前沿解（整数）
                 best_front_objs = current_front_objs  # 更新当前最优前沿目标值（整数）
                 # 记录收敛趋势（基于最优前沿的平均目标值，整数）
+                # 记录收敛趋势并判断早停条件
                 if len(current_front_objs) > 0:
-                    avg_cost = sum(obj[0] for obj in current_front_objs) // len(current_front_objs)  # 整数平均
-                    avg_tardiness = sum(obj[1] for obj in current_front_objs) // len(current_front_objs)  # 整数平均
+                    avg_cost = sum(obj[0] for obj in current_front_objs) // len(current_front_objs)
+                    avg_tardiness = sum(obj[1] for obj in current_front_objs) // len(current_front_objs)
                     convergence_history.append((avg_cost, avg_tardiness))
 
-                    # 判断是否改进（用于早停）
-                    current_avg = avg_cost + avg_tardiness  # 合并两个目标的平均值（整数）
-                    if abs(current_avg - prev_best_avg) < 1e-4:  # 变化小于阈值，视为无改进
-                        no_improve_count += 1
+                    # 早停判断逻辑
+                    if prev_avg_cost is not None and prev_avg_tardiness is not None:
+                        # 计算两个目标的变化量
+                        cost_change = abs(avg_cost - prev_avg_cost)
+                        tardiness_change = abs(avg_tardiness - prev_avg_tardiness)
+
+                        # 检查是否两个目标的变化都小于阈值
+                        if (cost_change < config.early_stop_threshold and
+                                tardiness_change < config.early_stop_threshold):
+                            no_improve_count += 1
+                        else:
+                            no_improve_count = 0  # 有改进，重置计数器
+                            prev_avg_cost = avg_cost
+                            prev_avg_tardiness = avg_tardiness
                     else:
-                        no_improve_count = 0  # 有改进，重置计数器
-                        prev_best_avg = current_avg
+                        # 初始化历史值（第一代）
+                        prev_avg_cost = avg_cost
+                        prev_avg_tardiness = avg_tardiness
+                        no_improve_count = 0
                 else:
                     no_improve_count += 1  # 无前沿解，视为无改进
+
+                # 早停检查（连续多代无改进则停止）
+                if no_improve_count >= config.early_stop_patience:
+                    print(
+                        f"早停触发：连续{no_improve_count}代两个目标值变化均小于{config.early_stop_threshold}，终止于第{generation}代")
+                    break
                 # 选择操作（锦标赛选择）
                 selected = nsga2.selection(population, objectives)
                 # 校验选择后的种群大小