灰狼优化算法+随机森林

### 灰狼优化算法结合随机森林进行机器学习模型构建和优化的方法 #### 1. 参数优化 灰狼优化算法可以用来优化随机森林中的超参数，从而提升模型性能。常见的可调参数包括树的数量、最大特征数、最小样本分割数量等。 对于这些参数的选择通常依赖于经验或网格搜索方法，但这种方法效率较低且可能陷入局部最优解。利用灰狼优化算法能够更高效地探索参数空间并找到全局最优解[^1]。 ```python from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score import numpy as np from sklearn.ensemble import RandomForestClassifier def objective_function(params): n_estimators, max_features, min_samples_split = params rf_model = RandomForestClassifier( n_estimators=int(n_estimators), max_features=max_features, min_samples_split=int(min_samples_split), random_state=42 ) X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42) rf_model.fit(X_train, y_train) predictions = rf_model.predict(X_val) return -accuracy_score(y_val, predictions) X, y = make_classification(n_samples=1000, n_features=20, random_state=42) bounds = [(50, 300), (0.1, 1.0), (2, 10)] # Define the search space for parameters using bounds. ``` #### 2. 特征选择 除了参数调整外，还可以应用灰狼优化来进行特征子集的选择。在这个过程中，个体代表不同的特征组合；适应度函数可以根据所选特征训练后的分类器表现定义，比如交叉验证得分。 通过这种方式选出最具影响力的特征集合，有助于减少过拟合风险以及加快计算过程，最终达到更好的泛化能力[^2]。 #### 3. 综合实例展示 下面给出一个简单的例子说明如何使用Python实现上述两种方式： - **数据准备** 创建一个人工二元分类问题的数据集作为演示用途。 - **初始化种群** 设定初始位置向量表示待寻优变量（即RF的超参），同时指定边界条件限制其取值范围。 - **迭代进化** 按照GWO流程不断更新α、β、δ三头领导狼的位置直到满足终止准则为止。 - **评估结果** ```python class GreyWolfOptimizer: def __init__(self, population_size, dimensions, lower_bound, upper_bound, iterations): self.population_size = population_size self.dimensions = dimensions self.lower_bound = lower_bound self.upper_bound = upper_bound self.iterations = iterations # Initialize wolves' positions randomly within given limits self.positions = np.random.uniform(low=self.lower_bound, high=self.upper_bound, size=(population_size, dimensions)) # Placeholder variables to store best solutions found so far during optimization process self.alpha_pos, self.beta_pos, self.delta_pos = None, None, None self.best_fitness_value = float('inf') def optimize(self, obj_func): for iteration in range(self.iterations): fitness_values = [] for i in range(len(self.positions)): current_position = self.positions[i] # Calculate fitness value of each wolf based on provided objective function fitness = obj_func(current_position) fitness_values.append(fitness) if fitness < self.best_fitness_value: self.best_fitness_value = fitness self.alpha_pos = list(current_position.copy()) sorted_indices = np.argsort(fitness_values)[:3] self.alpha_pos = self.positions[sorted_indices[0]].copy() self.beta_pos = self.positions[sorted_indices[1]].copy() self.delta_pos = self.positions[sorted_indices[2]].copy() a = 2 - iteration * ((2) / self.iterations) for i in range(0, self.population_size): A1 = 2*a*np.random.rand() - a C1 = 2*np.random.rand() D_alpha = abs(C1*self.alpha_pos - self.positions[i]) X1 = self.alpha_pos - A1*D_alpha r1 = np.random.rand() r2 = np.random.rand() A2 = 2*a*r1 - a C2 = 2*r2 D_beta = abs(C2*self.beta_pos - self.positions[i]) X2 = self.beta_pos - A2*D_beta r1 = np.random.rand() r2 = np.random.rand() A3 = 2*a*r1 - a C3 = 2*r2 D_delta = abs(C3*self.delta_pos - self.positions[i]) X3 = self.delta_pos - A3*D_delta self.positions[i] = (X1 + X2 + X3)/3 return self.alpha_pos, self.best_fitness_value if __name__ == "__main__": gwo_instance = GreyWolfOptimizer(population_size=20, dimensions=len(bounds), lower_bound=[b[0] for b in bounds], upper_bound=[b[1] for b in bounds], iterations=100) optimized_params, optimal_obj_value = gwo_instance.optimize(objective_function) print("Optimized Parameters:", ["{:.2f}".format(p) for p in optimized_params]) print("Best Objective Value Achieved:", "{:.4f}".format(-optimal_obj_value)) ```