IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v29y2018i5d10.1007_s10845-015-1164-z.html
   My bibliography  Save this article

Enhanced shuffled frog-leaping algorithm for solving numerical function optimization problems

Author

Listed:
  • Chao Liu

    (Yanshan University)

  • Peifeng Niu

    (Yanshan University
    National Engineering Research Center for Equipment and Technology of Cold Strip Rolling)

  • Guoqiang Li

    (Yanshan University)

  • Yunpeng Ma

    (Yanshan University)

  • Weiping Zhang

    (Qinhuangdao Institute of Technology)

  • Ke Chen

    (Yanshan University)

Abstract

The shuffled frog-leaping algorithm (SFLA) is a relatively new meta-heuristic optimization algorithm that can be applied to a wide range of problems. After analyzing the weakness of traditional SFLA, this paper presents an enhanced shuffled frog-leaping algorithm (MS-SFLA) for solving numerical function optimization problems. As the first extension, a new population initialization scheme based on chaotic opposition-based learning is employed to speed up the global convergence. In addition, to maintain efficiently the balance between exploration and exploitation, an adaptive nonlinear inertia weight is introduced into the SFLA algorithm. Further, a perturbation operator strategy based on Gaussian mutation is designed for local evolutionary, so as to help the best frog to jump out of any possible local optima and/or to refine its accuracy. In order to illustrate the efficiency of the proposed method (MS-SFLA), 23 well-known numerical function optimization problems and 25 benchmark functions of CEC2005 are selected as testing functions. The experimental results show that the enhanced SFLA has a faster convergence speed and better search ability than other relevant methods for almost all functions.

Suggested Citation

  • Chao Liu & Peifeng Niu & Guoqiang Li & Yunpeng Ma & Weiping Zhang & Ke Chen, 2018. "Enhanced shuffled frog-leaping algorithm for solving numerical function optimization problems," Journal of Intelligent Manufacturing, Springer, vol. 29(5), pages 1133-1153, June.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:5:d:10.1007_s10845-015-1164-z
    DOI: 10.1007/s10845-015-1164-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-015-1164-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-015-1164-z?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.

    Citations

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


    Cited by:

    1. Zhao, Ruxin & Wang, Yongli & Liu, Chang & Hu, Peng & Li, Yanchao & Li, Hao & Yuan, Chi, 2020. "Selfish herd optimizer with levy-flight distribution strategy for global optimization problem," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 538(C).
    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.

    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:joinma:v:29:y:2018:i:5:d:10.1007_s10845-015-1164-z. 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: 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.