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A hybrid gazelle optimization and reptile search algorithm for optimal clustering in wireless sensor networks
Soha S Elashry1, A S Abohamama1,2, Hatem Mohamed Abdul-Kader3
1Department of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt.
Scientific Reports
|April 26, 2025
Summary
A new hybrid meta-heuristic algorithm, HGORSA, enhances wireless sensor network (WSN) clustering by optimizing cluster head selection. This improves energy efficiency and network lifetime, outperforming existing methods in simulations.
Area of Science:
- Computer Science
- Network Engineering
- Artificial Intelligence
Background:
- Wireless Sensor Networks (WSNs) are crucial for modern applications but face challenges in energy management and extending network lifespan.
- Efficient clustering is vital for WSN performance, particularly in large-scale deployments.
- Existing meta-heuristic algorithms struggle to balance exploration and exploitation for optimal clustering.
Purpose of the Study:
- To introduce a novel hybrid meta-heuristic algorithm, HGORSA, for optimizing cluster head selection in WSNs.
- To enhance energy efficiency and prolong the network lifetime of WSNs through improved clustering.
- To evaluate the performance of HGORSA against state-of-the-art algorithms in various network configurations.
Main Methods:
- Developed HGORSA by integrating operators from the Gazelle Optimization Algorithm (GOA) and Reptile Search Algorithm (RSA) to improve exploration-exploitation balance.
- Simulated HGORSA with 300 sensor nodes and compared its performance against six other meta-heuristic algorithms.
- Conducted supplementary experiments with 50 and 500 sensor nodes to assess performance in dense and sparse networks.
- Validated HGORSA's robustness using statistical measures over 20 independent runs.
Main Results:
- HGORSA demonstrated superior performance in stability period, energy consumption, network lifetime, reduction in dead nodes, and network throughput compared to all benchmarked algorithms.
- Achieved significant percentage improvements across key metrics, for example, 37.3% in stability period and 10.8% in energy consumption against PSO.
- Consistently outperformed other algorithms in dense and sparse network scenarios and showed robust performance across multiple runs.
Conclusions:
- The proposed HGORSA algorithm effectively optimizes cluster head selection in WSNs.
- HGORSA significantly enhances energy management and extends network lifetime, addressing critical WSN challenges.
- HGORSA represents a promising advancement for efficient and robust wireless sensor network operations.
