Adaptive Coverage Optimization in Wireless Sensor Networks Using Hybrid Artificial Hummingbird Algorithm with Differential Evolution
DOI:
https://doi.org/10.22399/ijcesen.5292Keywords:
Bio-Inspired Algorithm, Hybrid Algorithm, Network Coverage, CEC Benchmarks, Artificial hummingbird algorithmAbstract
Optimizing sensor placement for maximum coverage in Wireless Sensor Networks (WSNs) remains a critical challenge, often leading to sub-optimal solutions when using standard metaheuristics. This paper introduces an Adaptive Hybrid Artificial Hummingbird and Differential Evolution Algorithm (AHADE) to solve the WSN coverage problem. The primary novelty of this work lies not in the hybridization itself, but in the proposed adaptive mechanism that intelligently governs the search process. By monitoring population diversity and solution convergence rate in real-time, AHADE strategically triggers the powerful mutation operators of Differential Evolution only when the search process shows signs of stagnation. This targeted intervention prevents premature convergence and maintains a robust balance between global exploration and local exploitation. The efficacy of AHADE was validated on the CEC 2020/2022 benchmarks and a practical WSN coverage problem. The results are compelling: AHADE achieved a network coverage of up to 95.3% in dense deployments, a statistically significant improvement over eleven other state-of-the-art algorithms. Furthermore, a comprehensive study confirmed the synergistic effect of the adaptive hybridization, with AHADE consistently outperforming its standalone parent algorithms. These findings validate AHADE as a powerful and intelligent optimization tool for complex real-world engineering problems.
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