IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i3p351-d732011.html

An Enhanced Grey Wolf Optimizer with a Velocity-Aided Global Search Mechanism

Author

Listed:
  • Farshad Rezaei

    (Department of Civil Engineering, Isfahan University of Technology, Isfahan 8415683111, Iran)

  • Hamid Reza Safavi

    (Department of Civil Engineering, Isfahan University of Technology, Isfahan 8415683111, Iran)

  • Mohamed Abd Elaziz

    (Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt
    Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt
    Artificial Intelligence Research Center (AIRC), Ajman University, Ajman P.O. Box 346, United Arab Emirates)

  • Shaker H. Ali El-Sappagh

    (Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt
    Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha 13518, Egypt)

  • Mohammed Azmi Al-Betar

    (Artificial Intelligence Research Center (AIRC), Ajman University, Ajman P.O. Box 346, United Arab Emirates
    Department of Information Technology, Al-Huson University College, Al-Balqa Applied University, Al-Huson, Irbid 21110, Jordan)

  • Tamer Abuhmed

    (College of Computing and Informatics, Sungkyunkwan University, Seoul 16419, Korea)

Abstract

This paper proposes a novel variant of the Grey Wolf Optimization (GWO) algorithm, named Velocity-Aided Grey Wolf Optimizer (VAGWO). The original GWO lacks a velocity term in its position-updating procedure, and this is the main factor weakening the exploration capability of this algorithm. In VAGWO, this term is carefully set and incorporated into the updating formula of the GWO. Furthermore, both the exploration and exploitation capabilities of the GWO are enhanced in VAGWO via stressing the enlargement of steps that each leading wolf takes towards the others in the early iterations while stressing the reduction in these steps when approaching the later iterations. The VAGWO is compared with a set of popular and newly proposed meta-heuristic optimization algorithms through its implementation on a set of 13 high-dimensional shifted standard benchmark functions as well as 10 complex composition functions derived from the CEC2017 test suite and three engineering problems. The complexity of the proposed algorithm is also evaluated against the original GWO. The results indicate that the VAGWO is a computationally efficient algorithm, generating highly accurate results when employed to optimize high-dimensional and complex problems.

Suggested Citation

  • Farshad Rezaei & Hamid Reza Safavi & Mohamed Abd Elaziz & Shaker H. Ali El-Sappagh & Mohammed Azmi Al-Betar & Tamer Abuhmed, 2022. "An Enhanced Grey Wolf Optimizer with a Velocity-Aided Global Search Mechanism," Mathematics, MDPI, vol. 10(3), pages 1-32, January.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:3:p:351-:d:732011
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/3/351/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/3/351/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Mahmoodabadi, M.J., 2023. "An optimal robust fuzzy adaptive integral sliding mode controller based upon a multi-objective grey wolf optimization algorithm for a nonlinear uncertain chaotic system," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    2. Li, Ronghua & Ding, Wu & Huang, Taiming & Xiao, Yufan & Ke, Rongyi & Zou, Pintao & Chen, Yiyu & Wan, Zhongmin & Wang, Xiaodong & Zeng, Wei, 2025. "Optimization of proton exchange membrane fuel cell stack manifold inlet geometry using velocity-assisted gray wolf optimization algorithm," Renewable Energy, Elsevier, vol. 253(C).
    3. Ruxin Zhao & Wei Wang & Tingting Zhang & Chang Liu & Lixiang Fu & Jiajie Kang & Hongtan Zhang & Yang Shi & Chao Jiang, 2025. "An Adaptive Differential Evolution Algorithm Based on Data Preprocessing Method and a New Mutation Strategy to Solve Dynamic Economic Dispatch Considering Generator Constraints," Computational Economics, Springer;Society for Computational Economics, vol. 66(1), pages 207-240, July.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    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:gam:jmathe:v:10:y:2022:i:3:p:351-:d:732011. 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: MDPI Indexing Manager The email address of this maintainer does not seem to be valid anymore. Please ask MDPI Indexing Manager to update the entry or send us the correct address (email available below). General contact details of provider: https://www.mdpi.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.