IDEAS home Printed from https://ideas.repec.org/a/spr/coopap/v53y2012i1p271-295.html
   My bibliography  Save this article

A multi-swarm PSO using charged particles in a partitioned search space for continuous optimization

Author

Listed:
  • Abbas El Dor
  • Maurice Clerc
  • Patrick Siarry

Abstract

Particle swarm optimization (PSO) is characterized by a fast convergence, which can lead the algorithms of this class to stagnate in local optima. In this paper, a variant of the standard PSO algorithm is presented, called PSO-2S, based on several initializations in different zones of the search space, using charged particles. This algorithm uses two kinds of swarms, a main one that gathers the best particles of auxiliary ones, initialized several times. The auxiliary swarms are initialized in different areas, then an electrostatic repulsion heuristic is applied in each area to increase its diversity. We analyse the performance of the proposed approach on a testbed made of unimodal and multimodal test functions with and without coordinate rotation and shift. The Lennard-Jones potential problem is also used. The proposed algorithm is compared to several other PSO algorithms on this benchmark. The obtained results show the efficiency of the proposed algorithm. Copyright Springer Science+Business Media, LLC 2012

Suggested Citation

  • Abbas El Dor & Maurice Clerc & Patrick Siarry, 2012. "A multi-swarm PSO using charged particles in a partitioned search space for continuous optimization," Computational Optimization and Applications, Springer, vol. 53(1), pages 271-295, September.
    • Handle: RePEc:spr:coopap:v:53:y:2012:i:1:p:271-295
    DOI: 10.1007/s10589-011-9449-4
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10589-011-9449-4
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10589-011-9449-4?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Julien Lepagnot & Amir Nakib & Hamouche Oulhadj & Patrick Siarry, 2010. "A New Multiagent Algorithm for Dynamic Continuous Optimization," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 1(1), pages 16-38, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Das, Kedar Nath & Parouha, Raghav Prasad, 2015. "An ideal tri-population approach for unconstrained optimization and applications," Applied Mathematics and Computation, Elsevier, vol. 256(C), pages 666-701.
    2. Loreta Saunoriene & Marius Saunoris & Minvydas Ragulskis, 2023. "Image Hiding in Stochastic Geometric Moiré Gratings," Mathematics, MDPI, vol. 11(8), pages 1-20, April.
    3. Kedar Nath Das & Raghav Prasad Parouha, 2016. "Optimization with a novel hybrid algorithm and applications," OPSEARCH, Springer;Operational Research Society of India, vol. 53(3), pages 443-473, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      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:spr:coopap:v:53:y:2012:i:1:p:271-295. 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.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

      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.