计算机科学 ›› 2009, Vol. 36 ›› Issue (11): 193-195.

• 人工智能 • 上一篇    下一篇

基于试探的变步长自适应粒子群算法

郑春颖,郑全弟,王晓丹,王玉冰   

  1. (空军工程大学导弹学院 三原713800)
  • 出版日期:2018-11-16 发布日期:2018-11-16
  • 基金资助:
    本文受国家自然基金(No.F0975026),陕西省自然科学研究计划项目(No.2007f19}资助。

Self-adaptive Particle Swarm OPtimization Algorithm Based on Tentative Adjusting Step Factor

ZHENG Chun-ying,ZHENG Quan-di,WANG Xiao-dan,WANG Yu-bing   

  • Online:2018-11-16 Published:2018-11-16

摘要: 针对粒子群算法容易陷入局部最优的缺陷,在分析·J: }}}因子在算法中的作用机理的基础上,设计了一个根据种群多样性和进化代数自适应调节的惯性因子,并运用试探法,通过变换搜索步长,提高算法的局部搜索能力。最后,给出了3个典型函数的模拟例子,通过与APSO的对比结果显示,改进后的算法其性能得到极大提高。

关键词: 粒子群算法,惯性因子,进化代数

Abstract: Aiming at premature defect and poor result of Particle Swarm Optimization algorithm, a new Self-adaptive inertia factor was designed according to diversity in the population and generation number based on analysing inertia factor's effect of algorithm. And through ploughing around adjusting step factors,the Particle's ability in local searching was enhanced. Three typical function tests were given. Comparing with APSO, the result indicates the effectiveness of this improvement.

Key words: Particle swarm optimization algorithm, Inertia factor, Generation number

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