Date of Publication :5th December 2025

Abstract: Wireless Sensor Networks (WSNs) play a vital role in modern applications such as environmental monitoring, healthcare, and surveillance, where energy efficiency and network longevity are key performance parameters. This paper presents a hybrid optimization approach combining the Modified Artificial Bee Colony (ABC) algorithm and the Modified Firefly Optimization Algorithm (FOA) to enhance cluster head (CH) selection for prolonged network lifetime and stability. The proposed Hybrid ABC–FOA (HMABCFA) model is mathematically formulated to minimize total energy consumption, inter-node distance, and latency while maximizing residual energy and network stability. In the hybrid method, the Modified ABC algorithm performs local search and candidate exploration, while the Modified FOA, enhanced with a tidal force mechanism, refines global search by avoiding premature convergence. The firefly-based refinement phase replaces the scout bee mechanism to enhance exploration, enabling more robust CH selection. Simulation results demonstrate significant performance improvements—23.21% in network lifetime, 19.84% in energy stability, and 22.88% reduction in latency—compared to conventional methods. Additionally, topology analysis comparing Gaussian and Torus network models reveals that the Torus topology achieves superior load balancing, energy uniformity, and communication efficiency. These findings confirm that the proposed HMABCFA approach effectively enhances energy efficiency, stability, and scalability in WSN environments.

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