Open Access

ARTICLE

Clustering in Sensor Networks Using Regional Hierarchical Optimization: A Hybrid LEACH-ACO-GA Approach

Maryem Lachgar^1,*, Mansour Lmkaiti¹, Ibtissam Larhlimi¹, Imad Aattouri², Hicham Ouchitachen¹, Hicham Mouncif¹

1 LIMATI Laboratory, Polydisciplinary Faculty of Beni Mellal, University Sultan Moulay Slimane, Beni Mellal, Morocco
2 LAMRI Laboratory, Polydisciplinary Faculty of Khouribga, University Sultan Moulay Slimane, Khouribga, Morocco

* Corresponding Author: Maryem Lachgar. Email:

(This article belongs to the Special Issue: Advanced Bio-Inspired Optimization Algorithms and Applications)

Computers, Materials & Continua 2026, 88(1), 60 https://doi.org/10.32604/cmc.2026.075234

Received 28 October 2025; Accepted 23 March 2026; Issue published 08 May 2026

Abstract

This study introduces a hybrid routing protocol, Low Energy Adaptive Clustering Hierarchy—Ant Colony Optimization—Genetic Algorithm (LEACH-ACO-GA), for wireless sensor networks. It combines regional ant colony optimization for cluster head selection with inter-cluster routing based on a genetic algorithm. The proposed method reduces energy consumption from 6.9 J (LEACH Classic) to 5.6 J (LEACH-ACO-GA) and decreases latency from 460 to 390 ms, while maintaining a packet delivery ratio of 0.97. These values are averaged over 70 rounds based on 30 independent simulation runs conducted on networks with 50 and 200 nodes. The hybrid method extends network lifetime by up to 50% compared to traditional LEACH and improves performance robustness in dense network environments. The results indicate that two-level metaheuristic optimization is effective for scalable and energy-efficient wireless sensor networks in Internet of Things scenarios.

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Keywords

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