IDEAS home Printed from https://ideas.repec.org/a/igg/jsir00/v5y2014i2p1-12.html
   My bibliography  Save this article

Diversity and Mechanisms in Swarm Intelligence

Author

Listed:
  • Xin-She Yang

    (School of Science and Technology, Middlesex University London, London, UK)

Abstract

Swarm intelligence based algorithms such as particle swarm optimization have become popular in the last two decades. Various new algorithms such as cuckoo search and bat algorithm also show promising efficiency. In all these algorithms, it is essential to maintain the balance of exploration and exploitation by controlling directly and indirectly the diversity of the population. Different algorithms may use different mechanisms to control such diversity. In this review paper, the author reviews and analyzes the roles of diversity and relevant mechanisms in swarm intelligence. The author also discuss parameter tuning and parameter control. In addition, the author highlights some key open questions in swarm intelligence.

Suggested Citation

  • Xin-She Yang, 2014. "Diversity and Mechanisms in Swarm Intelligence," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 5(2), pages 1-12, April.
    • Handle: RePEc:igg:jsir00:v:5:y:2014:i:2:p:1-12
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijsir.2014040101
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Xiaohui Shen & Yonggang Wu & Lingxi Li & Peng He & Tongxin Zhang, 2024. "A Novel Hybrid Algorithm Based on Beluga Whale Optimization and Harris Hawks Optimization for Optimizing Multi-Reservoir Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(12), pages 4883-4909, September.
    2. Yi Yu & Yonggang Wu & Binqi Hu & Xinglong Liu, 2018. "An enhanced artificial bee colony algorithm (EABC) for solving dispatching of hydro-thermal system (DHTS) problem," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-19, January.

    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:igg:jsir00:v:5:y:2014:i:2:p:1-12. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.