Title: Leveraging a hybrid whale-grey wolf optimisation algorithm to enhance fifth generation deployment efficiency in multi-access edge computing

Authors: B. Senthilkumar; U. Barakkath Nisha; V. Kalpana; Priscilla Joy; S. Gokila

Addresses: Department of Electronics and Communication Engineering, Kalaignar Karunanidhi Institute of Technology (KIT), Coimbatore, Tamil Nadu, India ' Department of Information Technology, Sri Krishna College of Engineering and Technology, Coimbatore, Tamil Nadu, India ' Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Coimbatore, Tamil Nadu, India ' Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India ' Department of Information Technology, Karpagam Institute of Technology, Coimbatore, Tamil Nadu, India

Abstract: In contemporary advanced manufacturing environments, deploying numerous intelligent mobile devices is crucial to meet the rising need for adaptable production capabilities. This research article focuses on addressing critical challenges in modern connected environments where intelligent mobile devices play a crucial role. These devices are equipped with a variety of sensors and constantly synchronise massive data sets. Furthermore, with the increasing importance of energy efficiency, current studies highlight energy use as a substantial expense. Nevertheless, previous research has mostly focused on analysing energy consumption in cloud servers, neglecting to include the energy usage associated with edge computing and underestimating the influence of various mobile devices. This becomes further crucial as interconnected settings increase. The research article presents an integer programming paradigm that addresses the difficulty of efficiently deploying MEC servers and fifth generation (5G) small cells. Given the NP-hard nature of edge server deployment, the research article proposes a novel Hybrid Whale-Grey Wolf Optimisation (HWGWO) algorithm. This metaheuristic algorithm combines the global exploration capabilities of WOA with the local search efficiency of GWO, thereby achieving a balanced and efficient search process.

Keywords: MEC; multi-access edge computing; HWGWO; hybrid whale-grey wolf optimisation.

DOI: 10.1504/IJGUC.2025.148541

International Journal of Grid and Utility Computing, 2025 Vol.16 No.5/6, pp.451 - 460

Received: 26 Jun 2024
Accepted: 18 Jul 2024
Published online: 11 Sep 2025 *

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