Open Access

REVIEW

Grey Wolf Optimizer for Cluster-Based Routing in Wireless Sensor Networks: A Methodological Survey

Mohammad Shokouhifar^1,*, Fakhrosadat Fanian², Mehdi Hosseinzadeh^3,4,*, Aseel Smerat^5,6, Kamal M. Othman⁷, Abdulfattah Noorwali⁷, Esam Y. O. Zafar⁷

1 Department of Electrical and Computer Engineering, Shahid Beheshti University, Tehran, 1983969411, Iran
2 Department of Computer Science, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, 7616913439, Iran
3 School of Engineering & Technology, Duy Tan University, Da Nang, 550000, Vietnam
4 Department of AI, School of Computer Science and Engineering, Galgotias University, Greater Noida, 203201, India
5 Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan
6 Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 602105, India
7 Department of Electrical Engineering, College of Engineering and Architecture, Umm Al-Qura University, Makkah, 21955, Saudi Arabia

* Corresponding Authors: Mohammad Shokouhifar. Email: ; Mehdi Hosseinzadeh. Email:

(This article belongs to the Special Issue: Engineering Applications of Discrete Optimization and Scheduling Algorithms)

Computer Modeling in Engineering & Sciences 2026, 146(1), 6 https://doi.org/10.32604/cmes.2026.073789

Received 25 September 2025; Accepted 29 December 2025; Issue published 29 January 2026

Abstract

Wireless Sensor Networks (WSNs) have become foundational in numerous real-world applications, ranging from environmental monitoring and industrial automation to healthcare systems and smart city development. As these networks continue to grow in scale and complexity, the need for energy-efficient, scalable, and robust communication protocols becomes more critical than ever. Metaheuristic algorithms have shown significant promise in addressing these challenges, offering flexible and effective solutions for optimizing WSN performance. Among them, the Grey Wolf Optimizer (GWO) algorithm has attracted growing attention due to its simplicity, fast convergence, and strong global search capabilities. Accordingly, this survey provides an in-depth review of the applications of GWO and its variants for clustering, multi-hop routing, and hybrid cluster-based routing in WSNs. We categorize and analyze the existing GWO-based approaches across these key network optimization tasks, discussing the different problem formulations, decision variables, objective functions, and performance metrics used. In doing so, we examine standard GWO, multi-objective GWO, and hybrid GWO models that incorporate other computational intelligence techniques. Each method is evaluated based on how effectively it addresses the core constraints of WSNs, including energy consumption, communication overhead, and network lifetime. Finally, this survey outlines existing gaps in the literature and proposes potential future research directions aimed at enhancing the effectiveness and real-world applicability of GWO-based techniques for WSN clustering and routing. Our goal is to provide researchers and practitioners with a clear, structured understanding of the current state of GWO in WSNs and inspire further innovation in this evolving field.

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