IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v675y2025ics0378437125004303.html
   My bibliography  Save this article

Collective dynamics of particle swarm optimization: A network science perspective

Author

Listed:
  • Deng, Lingyun
  • Liu, Sanyang

Abstract

Particle swarm optimization (PSO) is a cornerstone of evolutionary computation, yet its population dynamics and topological properties remain poorly understood beyond traditional stability analysis. This study presents the first network science-based investigation of PSO’s intrinsic topology, demonstrating that its network structure inherently exhibits small-world architecture and heavy-tailed degree distributions. Through systematic analysis of 13 benchmark functions – including 7 unimodal and 6 multimodal problems – we construct population communication networks where nodes represent particles and edges denote the interaction between individuals. This interdisciplinary lens provides a promising theoretical framework for analyzing evolutionary computation methods.

Suggested Citation

  • Deng, Lingyun & Liu, Sanyang, 2025. "Collective dynamics of particle swarm optimization: A network science perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 675(C).
    • Handle: RePEc:eee:phsmap:v:675:y:2025:i:c:s0378437125004303
    DOI: 10.1016/j.physa.2025.130778
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437125004303
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2025.130778?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:675:y:2025:i:c:s0378437125004303. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.