计算机工程 ›› 2010, Vol. 36 ›› Issue (4): 180-182.doi: 10.3969/j.issn.1000-3428.2010.04.063

• 人工智能及识别技术 • 上一篇    下一篇

基于Tent混沌序列的粒子群优化算法

田东平1,2   

  1. (1. 宝鸡文理学院计算机软件研究所,宝鸡 721007;2. 宝鸡文理学院计算信息科学研究所,宝鸡 721007)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2010-02-20 发布日期:2010-02-20

Particle Swarm Optimization Algorithm Based on Tent Chaotic Sequence

TIAN Dong-ping1,2   

  1. (1. Institute of Computer Software, Baoji University of Arts and Science, Baoji 721007;2. Institute of Computational Information Science, Baoji University of Arts and Science, Baoji 721007)
  • Received:1900-01-01 Revised:1900-01-01 Online:2010-02-20 Published:2010-02-20

摘要: 针对粒子群优化算法易陷入局部极值和进化后期收敛速度缓慢的问题,提出基于Tent混沌序列的粒子群优化算法,应用Tent映射初始化均匀分布的粒群,提高初始解的质量,设定粒子群聚集程度的判定阈值,并引入局部变异机制和局部应用Tent映射重新初始化粒群的方法,增强算法跳出局部最优解的能力,有效避免计算的盲目性,从而加快算法的收敛速度。仿真实验结果表明,该算法是有效的。

关键词: 粒子群优化算法, Tent映射, 变异机制, 判定阈值, 收敛速度

Abstract: Aiming at the problems of easily getting into the local optimum and slowly converging speed of the Particle Swarm Optimization(PSO) algorithm, a new PSO algorithm based on Tent chaotic sequence is proposed. The uniform particles are produced by Tent mapping so as to improve the quality of the initial solutions. The decision threshold of particles focusing degree is employed, and the local mutation mechanism and the local reinitializing particles are introduced in order to help the PSO algorithm to break away from the local optimum, whick can avoid the redundant computation and accelerate the convergence speed of the evolutionary process. Simulation experimental results show this algorithm is effective.

Key words: Particle Swarm Optimization(PSO) algorithm, Tent mapping, mutation mechanism, decision threshold, convergence speed

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