Particle Swarm Convergence: An Empirical Investigation

被引:0
|
作者
Cleghorn, Christopher W.
Engelbrecht, Andries P.
机构
关键词
STABILITY;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper performs a thorough empirical investigation of the conditions placed on particle swarm optimization control parameters to ensure convergent behavior. At present there exists a large number of theoretically derived parameter regions that will ensure particle convergence, however, selecting which region to utilize in practice is not obvious. The empirical study is carried out over a region slightly larger than that needed to contain all the relevant theoretically derived regions. It was found that there is a very strong correlation between one of the theoretically derived regions and the empirical evidence. It was also found that parameters near the edge of the theoretically derived region converge at a very slow rate, after an initial population explosion. Particle convergence is so slow, that in practice, the edge parameter settings should not really be considered useful as convergent parameter settings.
引用
收藏
页码:2524 / 2530
页数:7
相关论文
共 50 条
  • [41] A particle swarm optimization algorithm with empirical balance strategy
    Zhang Y.
    Kong X.
    Chaos, Solitons and Fractals: X, 2023, 10
  • [42] An empirical comparison of particle swarm and predator prey optimisation
    Silva, A
    Neves, A
    Costa, E
    ARTIFICIAL INTELLIGENCE AND COGNITIVE SCIENCE, PROCEEDINGS, 2002, 2464 : 103 - 110
  • [43] Empirical study of an unconstrained modified particle swarm optimization
    Moore, Phillip W.
    Venayagamoorthy, Ganesh K.
    2006 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-6, 2006, : 1462 - +
  • [44] Empirical Study of Simultaneous Perturbation Particle Swarm Optimization
    Maeda, Yutaka
    Matsushita, Naoto
    2008 PROCEEDINGS OF SICE ANNUAL CONFERENCE, VOLS 1-7, 2008, : 2444 - 2447
  • [45] The particle swarm optimization algorithm: convergence analysis and parameter selection
    Trelea, IC
    INFORMATION PROCESSING LETTERS, 2003, 85 (06) : 317 - 325
  • [46] The particle swarm - Explosion, stability, and convergence in a multidimensional complex space
    Clerc, M
    Kennedy, J
    IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2002, 6 (01) : 58 - 73
  • [47] Convergence Analysis of the Particle Swarm Optimization with Stochastic Inertia Weight
    Wang Qingguo
    Yan Wenjun
    Yao Wei
    2010 8TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA), 2010, : 356 - 361
  • [48] The Convergence of Particle Swarm with a Unified and Simplified Formula for Position Updating
    Hu, Jian
    2017 NINTH INTERNATIONAL CONFERENCE ON INTELLIGENT HUMAN-MACHINE SYSTEMS AND CYBERNETICS (IHMSC 2017), VOL 1, 2017, : 219 - 222
  • [49] Study of particle swarm optimization algorithm based on convergence control
    Liu, Dong
    Feng, Quan-Yuan
    Kongzhi yu Juece/Control and Decision, 2011, 26 (12): : 1917 - 1920
  • [50] Hybrid Particle Swarm Optimization and Convergence Analysis for Scheduling Problems
    Zhang, Xue-Feng
    Koshimura, Miyuki
    Fujita, Hiroshi
    Hasegawa, Ryuzo
    PROCEEDINGS OF THE FOURTEENTH INTERNATIONAL CONFERENCE ON GENETIC AND EVOLUTIONARY COMPUTATION COMPANION (GECCO'12), 2012, : 307 - 314