GWO算法的基本参数

GWO算法的基本参数包括种群大小、迭代次数、灰狼的位置范围和灰狼的初始位置。下面是一个示例代码，展示了如何使用Python实现GWO算法并设置这些基本参数：

import numpy as np

def gwo_algorithm(population_size, max_iterations, search_space):
    # 初始化灰狼的位置
    wolves_position = np.random.uniform(search_space[0], search_space[1], (population_size, len(search_space)))
    
    # 迭代优化过程
    for iteration in range(max_iterations):
        # 更新每个灰狼的位置
        for i in range(population_size):
            # 根据公式更新灰狼的位置
            a = 2 - iteration * ((2) / max_iterations)  # 衰减因子
            r1 = np.random.random(len(search_space))
            r2 = np.random.random(len(search_space))
            A1 = 2 * a * r1 - a
            C1 = 2 * r2
            D_alpha = abs(C1 * wolves_alpha - wolves_position[i])
            X1 = wolves_alpha - A1 * D_alpha
            
            r1 = np.random.random(len(search_space))
            r2 = np.random.random(len(search_space))
            A2 = 2 * a * r1 - a
            C2 = 2 * r2
            D_beta = abs(C2 * wolves_beta - wolves_position[i])
            X2 = wolves_beta - A2 * D_beta
            
            r1 = np.random.random(len(search_space))
            r2 = np.random.random(len(search_space))
            A3 = 2 * a * r1 - a
            C3 = 2 * r2
            D_delta = abs(C3 * wolves_delta - wolves_position[i])
            X3 = wolves_delta - A3 * D_delta
            
            # 更新灰狼的位置
            wolves_position[i] = (X1 + X2 + X3) / 3
        
        # 更新灰狼的适应度值
        wolves_fitness = calculate_fitness(wolves_position)
        
        # 更新灰狼的alpha、beta和delta
        alpha_index = np.argmin(wolves_fitness)
        beta_index = np.argsort(wolves_fitness)[1]
        delta_index = np.argsort(wolves_fitness)[2]
        wolves_alpha = wolves_position[alpha_index]
        wolves_beta = wolves_position[beta_index]
        wolves_delta = wolves_position[delta_index]
    
    # 返回最优解
    best_solution = wolves_alpha
    best_fitness = np.min(wolves_fitness)
    return best_solution, best_fitness

# 设置基本参数
population_size = 50
max_iterations = 100
search_space = [-10, 10]

# 调用GWO算法
best_solution, best_fitness = gwo_algorithm(population_size, max_iterations, search_space)

# 输出结果
print("Best solution:", best_solution)
print("Best fitness:", best_fitness)