IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/592682.html
   My bibliography  Save this article

Quantum Behaved Particle Swarm Optimization Algorithm Based on Artificial Fish Swarm

Author

Listed:
  • Dong Yumin
  • Zhao Li

Abstract

Quantum behaved particle swarm algorithm is a new intelligent optimization algorithm; the algorithm has less parameters and is easily implemented. In view of the existing quantum behaved particle swarm optimization algorithm for the premature convergence problem, put forward a quantum particle swarm optimization algorithm based on artificial fish swarm. The new algorithm based on quantum behaved particle swarm algorithm, introducing the swarm and following activities, meanwhile using the adaptive parameters, to avoid it falling into local extremum of population. The experimental results show the improved algorithm to improve the optimization ability of the algorithm.

Suggested Citation

  • Dong Yumin & Zhao Li, 2014. "Quantum Behaved Particle Swarm Optimization Algorithm Based on Artificial Fish Swarm," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-10, August.
    • Handle: RePEc:hin:jnlmpe:592682
    DOI: 10.1155/2014/592682
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2014/592682.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2014/592682.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2014/592682?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
    ---><---

    More about this item

    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:hin:jnlmpe:592682. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.