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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
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    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).

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