粒子群算法伪代码python

粒子群优化（Particle Swarm Optimization, PSO）是一种模拟鸟群或鱼群觅食行为的搜索算法，常用于解决优化问题。以下是简化的Python伪代码表示：

# 初始化粒子位置和速度
def initialize_particles(num_particles, bounds):
    particles = [{'position': np.random.uniform(bounds, bounds), 'velocity': np.zeros(len(bounds)),
                  'best_position': copy.deepcopy(particles['position']), 'fitness': np.inf} for _ in range(num_particles)]
    return particles

# 更新粒子的位置和速度
def update_velocity_and_position(particles, best_positions, inertia=0.7, c1=2, c2=2, w=0.7):
    for particle in particles:
        r1, r2 = np.random.rand(), np.random.rand() # 随机数
        particle['velocity'] = w * particle['velocity'] + c1 * r1 * (particle['best_position'] - particle['position']) + \
                              c2 * r2 * (best_positions['global_best_position'] - particle['position'])
        particle['position'] += particle['velocity']
        # 确保粒子在边界范围内
        particle['position'] = clipToBounds(particle['position'], bounds)

# 计算并更新最佳位置和全局最佳位置
def evaluate_fitness(particles, objective_function):
    for particle in particles:
        particle['fitness'] = objective_function(particle['position'])
        if particle['fitness'] < particle['best_fitness']:
            particle['best_position'] = particle['position']
            global_best_particle = min(particles, key=lambda p: p['fitness'])['position']
    return particles, {'global_best_position': global_best_particle}

# 主循环
def pso(optimizer, num_iterations, objective_function, bounds):
    particles = initialize_particles(optimizer.num_particles, bounds)
    for i in range(num_iterations):
        particles, best_positions = evaluate_fitness(particles, objective_function)
        update_velocity_and_position(particles, best_positions)
    return best_positions['global_best_position']

# 使用函数
optimizer = {'num_particles': 50}  # 可调整参数
solution = pso(optimizer, 100, your_objective_function, your_bounds)