粒子群优化算法的伪代码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，用于解决优化问题。以下是基于Python的简单粒子群优化算法的伪代码：

def pso(population_size, dimensions, max_velocity, max_iterations, c1, c2, w):
    # 初始化粒子位置和速度
    particles = [{'position': [random.uniform(0, 1) for _ in range(dimensions)],
                  'velocity': [0 for _ in range(dimensions)],
                  'best_position': list(position),
                  'fitness': calculate_fitness(particles[0]['position'])} for _ in range(population_size)]
    
    # 初始化全局最佳解
    global_best = min(particles, key=lambda particle: particle['fitness'])

    for iteration in range(max_iterations):
        for i, particle in enumerate(particles):
            # 更新粒子的速度
            r1, r2 = random.random(), random.random()
            particle['velocity'] = w * particle['velocity'] + c1 * r1 * (particle['best_position'] - particle['position']) \
                                 + c2 * r2 * (global_best['position'] - particle['position'])
            
            # 更新粒子的位置
            if within_bounds(particle['position'], dimensions):  # 检查是否超出边界
                particle['position'] = update_position(particle['position'], particle['velocity'])
                
            # 计算新位置的适应度
            particle['fitness'] = calculate_fitness(particle['position'])
            
            # 更新局部和全局最优解
            if particle['fitness'] < particle['best_position']:
                particle['best_position'] = list(particle['position'])
            
            if particle['fitness'] < global_best['fitness']:
                global_best = particle

    return global_best, particles

# 辅助函数
def within_bounds(position, dimensions):
    return all(0 <= value <= 1 for value in position)

def calculate_fitness(position):
    # 这里替换为实际问题的目标函数计算
    pass

def update_position(current_position, velocity):
    return [current_position[i] + velocity[i] for i in range(len(current_position))]

# 调用函数并处理结果
best_solution, particle_history = pso(population_size=100, dimensions=2, ... )