设计求解下列优化问题的PSO算法：minf(x)=x1**2+x2**2+25*（sin(x1)**2+sin(x2)**2),-3<=xi<=3,

以下是使用粒子群优化算法（PSO）求解优化问题的代码示例：

import numpy as np

def objective_function(x):
    return x[0]**2 + x[1]**2 + 25*(np.sin(x[0])**2 + np.sin(x[1])**2)

def pso_algorithm():
    # 参数设置
    num_particles = 50  # 粒子数量
    num_dimensions = 2  # 维度数量
    max_iterations = 100  # 最大迭代次数
    inertia_weight = 0.7  # 惯性权重
    cognitive_weight = 1.4  # 认知权重
    social_weight = 1.4  # 社会权重

    # 初始化粒子位置和速度
    particles_position = np.random.uniform(low=-3, high=3, size=(num_particles, num_dimensions))
    particles_velocity = np.zeros((num_particles, num_dimensions))

    # 初始化全局最优解和对应的适应度值
    global_best_position = np.zeros(num_dimensions)
    global_best_fitness = float('inf')

    # 迭代优化
    for iteration in range(max_iterations):
        for i in range(num_particles):
            # 计算粒子的适应度值
            fitness = objective_function(particles_position[i])

            # 更新个体最优解
            if fitness < global_best_fitness:
                global_best_fitness = fitness
                global_best_position = particles_position[i]

            # 更新粒子速度和位置
            particles_velocity[i] = (inertia_weight * particles_velocity[i] +
                                     cognitive_weight * np.random.rand() * (particles_position[i] - particles_position[i]) +
                                     social_weight * np.random.rand() * (global_best_position - particles_position[i]))
            particles_position[i] += particles_velocity[i]

            # 限制粒子位置在范围内
            particles_position[i] = np.clip(particles_position[i], -3, 3)

    return global_best_position, global_best_fitness

# 调用PSO算法求解优化问题
best_position, best_fitness = pso_algorithm()
print("最优解：", best_position)
print("最优解对应的适应度值：", best_fitness)