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

Farshad Rezaei, Hamid Reza Safavi, Mohamed Abd Elaziz, Shaker H. Ali El-Sappagh, Mohammed Azmi Al-Betar and Tamer Abuhmed
Additional contact information
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
Shaker H. Ali El-Sappagh: Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt
Mohammed Azmi Al-Betar: Artificial Intelligence Research Center (AIRC), Ajman University, Ajman P.O. Box 346, United Arab Emirates
Tamer Abuhmed: College of Computing and Informatics, Sungkyunkwan University, Seoul 16419, Korea

Mathematics, 2022, vol. 10, issue 3, 1-32

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.

Keywords: optimization; meta-heuristic algorithms; swarm intelligence algorithms; global search; exploration; exploitation; grey wolf optimizer (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/3/351/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/3/351/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:3:p:351-:d:732011

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().