IDEAS home Printed from https://ideas.repec.org/a/igg/jsir00/v9y2018i1p1-19.html
   My bibliography  Save this article

A Quantum Particle Swarm Optimization Algorithm Based on Self-Updating Mechanism

Author

Listed:
  • Shuyue Wu

    (School of Information Science & Engineering, Hunan International Economics University, Changsha, China)

Abstract

The living mechanism has limited life in nature; it will age and die with time. This article describes that during the progressive process, the aging mechanism is very important to keep a swarm diverse. In the quantum behavior particle swarm (QPSO) algorithm, the particles are aged and the algorithm is prematurely convergent, the self-renewal mechanism of life is introduced into QPSO algorithm, and a leading particle and challengers are introduced. When the population particles are aged and the leading power of leading particle is exhausted, a challenger particle becomes the new leader particle through the competition update mechanism, group evolution is completed and the group diversity is maintained, and the global convergence of the algorithm is proven. Next in the article, twelve Clement2009 benchmark functions are used in the experimental test, both the comparison and analysis of results of the proposed method and classical improved QPSO algorithms are given, and the simulation results show strong global finding ability of the proposed algorithm. Especially in the seven multi-model test functions, the comprehensive performance is optimal.

Suggested Citation

  • Shuyue Wu, 2018. "A Quantum Particle Swarm Optimization Algorithm Based on Self-Updating Mechanism," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 9(1), pages 1-19, January.
    • Handle: RePEc:igg:jsir00:v:9:y:2018:i:1:p:1-19
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJSIR.2018010101
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Sadik Kamel Gharghan & Saleem Latteef Mohammed & Ali Al-Naji & Mahmood Jawad Abu-AlShaeer & Haider Mahmood Jawad & Aqeel Mahmood Jawad & Javaan Chahl, 2018. "Accurate Fall Detection and Localization for Elderly People Based on Neural Network and Energy-Efficient Wireless Sensor Network," Energies, MDPI, vol. 11(11), pages 1-32, October.
    2. Lin Sun & Suisui Chen & Jiucheng Xu & Yun Tian, 2019. "Improved Monarch Butterfly Optimization Algorithm Based on Opposition-Based Learning and Random Local Perturbation," Complexity, Hindawi, vol. 2019, pages 1-20, February.

    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:9:y:2018:i:1:p:1-19. 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.