中文摘要：

粒子群优化（PSO）算法是一种新兴的群体智能优化技术，其由于具有原理简单、参数少、效果好等优点已获得广泛研究和应用．粒子个体极值更新速率低是影响该算法收敛速度和精度的主要因素之一．本文提出一种新型多步式位置可选择更新的粒子群算法，把标准粒子群中速度的单步更新公式分解成三步更新，取所生成的3个位置中的最好位置作为最终结果，细化了粒子的搜索轨迹、在不增加算法复杂度条件下提高了个体极值以及全局极值的更新速率，因而改善了算法的收敛速度和精度．采用Sphere、Rosenbrock等6个经典测试函数，并按照固定迭代次数运行和固定时间长度运行两种方法进行测试．测试结果表明该算法简单、稳健、高效，而且明显优于现有的4种经典粒子群算法．

英文摘要：

Particle swarin optimization （PSO） algorithm is a new promising swarm intelligence optimization technology, and it has been extensively studied and applied because of its advantages of simpler theory, less parameters and better performance. However,each particle＇ s individual minimum has a low updating rate, which has been one disadvantageous factor to affect this algorithm speed and precision. In this paper, we propose a novel multi-step position-selectable updating PSO algorithm. This algorithm decomposes the standard PSO velocity single-step updating formula into three steps and selects the best one among the three resultant positions as the final updated position. This scheme refines each particle searching trajectory, increases the updating speed of individual and global minimums,and consequently improves PSO algorithm converging speed and precision without increasing the computing complexity. Six classical testing functions, including Sphere, Rosenbrock and so on, are used to verify the proposed algorithm in two ways： a fixed iteration number test and a Fixed lime length test. Large numbers of simulations show that the proposed algorithm is simple, robust, and efficient, and meanwhile it outperforms other four existing classical algorithms.