狼群算法python代码

以下是狼群算法的 Python 代码实现：

import random
import math

# 目标函数
def objective_function(x):
    return sum([i ** 2 for i in x])

# 初始化狼群
def initialize(num_dimensions, num_wolves):
    wolves = []
    for i in range(num_wolves):
        wolf = [random.uniform(-100, 100) for _ in range(num_dimensions)]
        wolves.append(wolf)
    return wolves

# 计算适应度
def calculate_fitness(position):
    return objective_function(position)

# 找到最优狼
def get_alpha(wolves):
    alpha_fitness = float("inf")
    for wolf in wolves:
        fitness = calculate_fitness(wolf)
        if fitness < alpha_fitness:
            alpha_fitness = fitness
            alpha = wolf
    return alpha

# 找到次优狼
def get_beta(wolves, alpha):
    beta_fitness = float("inf")
    for wolf in wolves:
        fitness = calculate_fitness(wolf)
        if fitness < beta_fitness and wolf != alpha:
            beta_fitness = fitness
            beta = wolf
    return beta

# 找到最劣狼
def get_delta(wolves, alpha, beta):
    delta_fitness = float("inf")
    for wolf in wolves:
        fitness = calculate_fitness(wolf)
        if fitness < delta_fitness and wolf != alpha and wolf != beta:
            delta_fitness = fitness
            delta = wolf
    return delta

# 更新狼的位置
def update_position(wolf, alpha, beta, delta, a, c):
    for i in range(len(wolf)):
        x = wolf[i]
        r1 = random.random()
        r2 = random.random()
        A = 2 * a * r1 - a
        C = 2 * c * r2 - c
        D_alpha = abs(C * alpha[i] - x)
        D_beta = abs(C * beta[i] - x)
        D_delta = abs(C * delta[i] - x)
        X1 = alpha[i] - A * D_alpha
        X2 = beta[i] - A * D_beta
        X3 = delta[i] - A * D_delta
        wolf[i] = (X1 + X2 + X3) / 3
    return wolf

# 狼群算法
def wolf_pack_algorithm(num_dimensions, num_wolves, num_iterations, a=1.5, c=1):
    wolves = initialize(num_dimensions, num_wolves)
    alpha = get_alpha(wolves)
    beta = get_beta(wolves, alpha)
    delta = get_delta(wolves, alpha, beta)
    for j in range(num_iterations):
        for i in range(num_wolves):
            wolf = wolves[i]
            wolf = update_position(wolf, alpha, beta, delta, a, c)
            wolves[i] = wolf
        alpha = get_alpha(wolves)
        beta = get_beta(wolves, alpha)
        delta = get_delta(wolves, alpha, beta)
    return alpha

使用示例：

num_dimensions = 2
num_wolves = 10
num_iterations = 100

best_solution = wolf_pack_algorithm(num_dimensions, num_wolves, num_iterations)
print("Best solution:", best_solution)
print("Objective function value:", objective_function(best_solution))

注意事项：

- 参数 `num_dimensions` 表示目标函数的自变量个数，这里默认为 2。
- 参数 `num_wolves` 表示狼群的大小，这里默认为 10。
- 参数 `num_iterations` 表示迭代次数，这里默认为 100。
- 参数 `a` 和 `c` 是算法中的两个常数，它们的默认值分别为 1.5 和 1。如果需要调整算法的收敛速度和精度，可以尝试修改这两个参数。