diff --git a/可持续旅游业模型(2).py b/可持续旅游业模型(2).py
index c41acce..ea2fefd 100644
--- a/可持续旅游业模型(2).py
+++ b/可持续旅游业模型(2).py
@@ -9,6 +9,8 @@ from pymoo.operators.crossover.sbx import SBX
 from pymoo.operators.mutation.pm import PM
 from pymoo.termination import get_termination
 from sklearn.gaussian_process import GaussianProcessRegressor
+from sklearn.gaussian_process.kernels import RBF
+from sklearn.linear_model import Ridge
 
 # 动态模型参数
 alpha = 0.05  # 冰川吸引力系数
@@ -93,63 +95,97 @@ class SustainableTourismProblem(Problem):
 
 
 # MOEA/D-EGO算法
-def moead_ego(problem, n_init=50, n_iter=200):
-    ref_dirs = get_reference_directions("das-dennis", 3, n_partitions=18)
+def moead_ego(problem, n_init=50, n_iter=100):
+    ref_dirs = get_reference_directions("das-dennis", 3, n_partitions=24)
 
-    # 初始样本生成
-    X_init = LHS().do(problem, n_init).get("X")
+    # 修改 LHS 采样设置
+    sampling = LHS()  # 移除有问题的参数
+    X_init = sampling.do(problem, n_init).get("X")
     F_init, G_init = problem.evaluate(X_init, return_values_of=["F", "G"])
 
-    # 代理模型
+    # 代理模型优化
     class SurrogateModel:
         def __init__(self):
             self.models = {
-                'f1': GaussianProcessRegressor(),
-                'f2': GaussianProcessRegressor(),
-                'f3': GaussianProcessRegressor(),
-                'g1': GaussianProcessRegressor(),
-                'g2': GaussianProcessRegressor(),
-                'g3': GaussianProcessRegressor(),
-                'g4': GaussianProcessRegressor(),
-                'g5': GaussianProcessRegressor(),
-                'g6': GaussianProcessRegressor()
+                'f1': GaussianProcessRegressor(
+                    kernel=1.0 * RBF(length_scale=[1.0] * 4, length_scale_bounds=(1e-3, 1e3)),
+                    n_restarts_optimizer=2,
+                    normalize_y=True,
+                    random_state=42
+                ),
+                'f2': GaussianProcessRegressor(
+                    kernel=1.0 * RBF(length_scale=[1.0] * 4, length_scale_bounds=(1e-3, 1e3)),
+                    n_restarts_optimizer=2,
+                    normalize_y=True,
+                    random_state=42
+                ),
+                'f3': GaussianProcessRegressor(
+                    kernel=1.0 * RBF(length_scale=[1.0] * 4, length_scale_bounds=(1e-3, 1e3)),
+                    n_restarts_optimizer=2,
+                    normalize_y=True,
+                    random_state=42
+                ),
+            }
+            # 约束条件使用简单的线性回归
+            self.constraint_models = {
+                f'g{i+1}': Ridge(alpha=1.0) for i in range(6)
             }
 
         def fit(self, X, F, G):
-            self.models['f1'].fit(X, F[:, 0])
-            self.models['f2'].fit(X, F[:, 1])
-            self.models['f3'].fit(X, F[:, 2])
+            # 标准化输入
+            self.X_mean = X.mean(axis=0)
+            self.X_std = X.std(axis=0)
+            X_norm = (X - self.X_mean) / self.X_std
+
+            for i, model in enumerate(['f1', 'f2', 'f3']):
+                self.models[model].fit(X_norm, F[:, i])
+            
             for i in range(6):
-                self.models[f'g{i + 1}'].fit(X, G[:, i])
+                self.constraint_models[f'g{i+1}'].fit(X_norm, G[:, i])
 
         def predict(self, X):
-            f1_pred = self.models['f1'].predict(X)
-            f2_pred = self.models['f2'].predict(X)
-            f3_pred = self.models['f3'].predict(X)
-            g_pred = np.column_stack([self.models[f'g{i + 1}'].predict(X) for i in range(6)])
+            X_norm = (X - self.X_mean) / self.X_std
+            f1_pred = self.models['f1'].predict(X_norm)
+            f2_pred = self.models['f2'].predict(X_norm)
+            f3_pred = self.models['f3'].predict(X_norm)
+            
+            g_pred = np.column_stack([
+                self.constraint_models[f'g{i+1}'].predict(X_norm) 
+                for i in range(6)
+            ])
+            
             return f1_pred, f2_pred, f3_pred, g_pred
 
     surrogate = SurrogateModel()
     surrogate.fit(X_init, F_init, G_init)
 
-    # MOEA/D配置
+    # MOEA/D配置优化
     algorithm = MOEAD(
         ref_dirs=ref_dirs,
-        n_neighbors=15,
+        n_neighbors=25,
         decomposition="tchebi",
-        prob_neighbor_mating=0.9,
-        sampling=LHS(),
-        crossover=SBX(prob=0.9, eta=15),
-        mutation=PM(eta=20),
+        prob_neighbor_mating=0.95,
+        sampling=sampling,
+        crossover=SBX(prob=0.95, eta=20),
+        mutation=PM(eta=25),
     )
 
-    for _ in range(n_iter):
-        # 生成候选解并预测
-        X_candidate = LHS().do(problem, 100).get("X")
-        f1_pred, f2_pred,f3_pred, g_pred = surrogate.predict(X_candidate)
-
-        # 计算期望改进（EI）
-        improvement = (f1_pred.min() - f1_pred) + (f2_pred.min() - f2_pred)+(f3_pred.min() - f3_pred)
+    # 记录最优解历史
+    best_f = float('inf')
+    no_improve_count = 0
+    
+    print("开始优化...")
+    for iter_num in range(n_iter):
+        X_candidate = LHS().do(problem, 50).get("X")  # 减少候选解数量
+        f1_pred, f2_pred, f3_pred, g_pred = surrogate.predict(X_candidate)
+        
+        # 综合考虑多个目标
+        improvement = -(f1_pred + f2_pred + f3_pred)  # 负号是因为我们要最小化
+        
+        # 添加约束惩罚
+        penalty = np.sum(np.maximum(g_pred, 0), axis=1)
+        improvement -= 100 * penalty  # 大的惩罚系数
+        
         best_idx = np.argmax(improvement)
         X_new = X_candidate[best_idx].reshape(1, -1)
 
@@ -159,7 +195,16 @@ def moead_ego(problem, n_init=50, n_iter=200):
         F_init = np.vstack([F_init, F_new])
         G_init = np.vstack([G_init, G_new])
         surrogate.fit(X_init, F_init, G_init)
-
+        
+        # 提前停止条件
+        if iter_num > 10 and np.max(improvement) < 1e-4:
+            print(f"在第{iter_num}次迭代达到收敛")
+            break
+            
+        if (iter_num + 1) % 5 == 0:  # 更频繁地打印进度
+            print(f"完成迭代: {iter_num + 1}/{n_iter}")
+
+    print("优化完成")
     return X_init, F_init, G_init