量子粒子群 python代码

以下是一个简单的量子粒子群算法的 Python 代码示例：

import numpy as np
# 目标函数
def fitness_func(x):
    return np.sum(x**2)

# 初始化粒子群
def init_swarm(n_particles, n_dimensions):
    swarm = np.random.rand(n_particles, n_dimensions)
    return swarm

# 更新速度和位置
def update(swarm, velocity, pbest, gbest, w, c1, c2):
    r1 = np.random.rand(*swarm.shape)
    r2 = np.random.rand(*swarm.shape)
    velocity = w * velocity + c1 * r1 * (pbest - swarm) + c2 * r2 * (gbest - swarm)
    swarm = swarm + velocity
    return swarm, velocity

# 量子粒子群算法
def qpswarm(n_particles, n_dimensions, max_iter):
    # 初始化粒子群
    swarm = init_swarm(n_particles, n_dimensions)
    velocity = np.zeros_like(swarm)
    pbest = swarm.copy()
    pbest_fitness = np.zeros(n_particles)
    gbest = np.zeros(n_dimensions)
    gbest_fitness = np.inf

    # 迭代
    for i in range(max_iter):
        # 计算适应度
        fitness = np.apply_along_axis(fitness_func, 1, swarm)
        # 更新个体最优解
        mask = fitness < pbest_fitness
        pbest[mask] = swarm[mask]
        pbest_fitness[mask] = fitness[mask]
        # 更新全局最优解
        idx = np.argmin(pbest_fitness)
        if pbest_fitness[idx] < gbest_fitness:
            gbest = pbest[idx]
            gbest_fitness = pbest_fitness[idx]
        # 更新速度和位置
        swarm, velocity = update(swarm, velocity, pbest, gbest, 0.5, 1.0, 1.0)

    return gbest, gbest_fitness

# 示例
gbest, gbest_fitness = qpswarm(50, 10, 100)
print("Global best solution:", gbest)
print("Global best fitness:", gbest_fitness)