Abstract: An improved Particle Swarm Optimization（PSO） algorithm is presented based on three methods of improvement in standard PSO to avoid being trapped in local minima.The iteration formula of PSO is changed and simplified by removal of velocity parameter that is unnecessary during the course of evolution.The personal best value of each particle is replaced by the mean value of them of all particles.The acceleration coefficients are adaptively adjusted to improve the search performance of algorithm.The experimental results show that the proposed algorithm not only has great advantages of convergence property over standard PSO and some other modified PSO algorithms，but also avoids effectively being trapped in local minima.

Key words: Particle Swarm Optimization（PSO）, local minima, personal best value, acceleration coefficients

摘要： 针对基本粒子群优化算法易于陷入局部最优的问题，提出了一种自适应扩展的简化粒子群优化算法。该算法采用去除速度项的简化算法结构，并用所有粒子个体极值的平均值代替每个粒子的个体极值，自适应动态调整加速系数。实验结果表明，算法能够有效避免早熟收敛问题，其全局收敛性能显著提高，收敛速度更快。

关键词: 粒子群优化算法, 局部最优, 个体极值, 加速系数