python GWO-PSO

# Python GWO-PSO示例
# 这是一个简单的Python示例，演示了GWO-PSO算法的实现。

# 导入所需的库
import numpy as np

# 初始化参数
num_wolves = 5
num_dimensions = 10
max_iter = 100
c1 = 2.0
c2 = 2.0
w = 0.7

# 初始化狼群位置
wolves_position = np.random.uniform(-10, 10, (num_wolves, num_dimensions))

# 初始化速度
velocity = np.zeros((num_wolves, num_dimensions))

# 初始化个体最佳位置和适应度
p_best_position = wolves_position
p_best_fitness = np.inf

# 初始化全局最佳位置和适应度
g_best_position = np.zeros(num_dimensions)
g_best_fitness = np.inf

# 开始迭代
for t in range(max_iter):
    for i in range(num_wolves):
        fitness = calculate_fitness(wolves_position[i])
        
        # 更新个体最佳位置和适应度
        if fitness < p_best_fitness:
            p_best_fitness = fitness
            p_best_position[i] = wolves_position[i]
        
        # 更新全局最佳位置和适应度
        if fitness < g_best_fitness:
            g_best_fitness = fitness
            g_best_position = wolves_position[i]
    
    # 更新速度和位置
    for i in range(num_wolves):
        r1 = np.random.rand(num_dimensions)
        r2 = np.random.rand(num_dimensions)
        velocity[i] = w * velocity[i] + c1 * r1 * (p_best_position[i] - wolves_position[i]) + c2 * r2 * (g_best_position - wolves_position[i])
        wolves_position[i] = wolves_position[i] + velocity[i]

# 输出全局最佳位置和适应度
print("Global best position: ", g_best_position)
print("Global best fitness: ", g_best_fitness)