《计算机应用研究》|Application Research of Computers

动态学习混沌映射的粒子群算法

Chaotic mapping particle swarm optimization algorithm based on variable learning factors

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作者 董丽凤,陈阳,巫光福
机构 江西理工大学 信息工程学院,江西 赣州 341000
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文章编号 1001-3695(2019)05-009-1319-04
DOI 10.19734/j.issn.1001-3695.2017.11.0737
摘要 传统粒子群优化算法(PSO)对社会认知部分与自我认知部分都采用恒定学习常数,一定程度上限制了种群全局协调能力。在算法收敛后期种群多样性丧失而导致全部个体收敛于搜索空间中的某一点,这易诱发早熟现象。针对这种缺陷提出一种动态学习混沌映射的粒子群优化算法(VLCMPSO)。在算法初期迭代中应多考虑自身记录的最佳点,在算法后期应快速向种群最佳点收敛,因而设计一种进行协调的动态学习因子。为克服早熟现象,判断种群多样性方差低于设定阈值时,以混沌映射的方式将该代最优个体位置更新且以新的方式进行优化操作。经实验证明新算法在收敛速度与精度上都具有更好的性能。
关键词 粒子群优化; 动态学习因子; 混沌映射; 全局优化
基金项目 国家自然科学基金资助项目(11461031)
江西省教育厅项目(GJJ14465)
本文URL http://www.arocmag.com/article/01-2019-05-009.html
英文标题 Chaotic mapping particle swarm optimization algorithm based on variable learning factors
作者英文名 Dong Lifeng, Chen Yang, Wu Guangfu
机构英文名 School of Information Engineering,Jiangxi University of Science & Technology,Ganzhou Jiangxi 341000,China
英文摘要 The traditional particle swarm optimization algorithm uses constant learning constants for social and self -cognition to limit the population's global coordination ability. In the late convergence of the algorithm, the diversity of the population was lost and all the individuals converge to one point in search space, which could trigger the precocious convergence. In view of this defect, this paper proposed a chaotic map particle swarm optimization algorithm based on variable learning factor. In the early stage of the algorithm, the emphases should focus on the best location of self-recording. At the later period of the algorithm, it should design a coordinated dynamic learning factor to converge on the best position of population. In order to overcome the premature phenomenon and determine the variance of population diversity below the set value, it used chaotic mapping updated the optimal individual location of the generation and utilized a new way to optimized. The experimental show the new algorithm has better performance in convergence speed and precision.
英文关键词 particle swarm optimization(PSO); variable learning factor; chaotic mapping; global optimization
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收稿日期 2017/11/6
修回日期 2017/12/25
页码 1319-1322
中图分类号 TP301.6;TP18
文献标志码 A