Quantum Leaping Grey Wolf Optimization for Energy Efficient Clustering to Improve the Operating Efficiency of Wireless Body Area Networks

This paper introduces a Quantum Leaping Grey Wolverine Optimization (QLGWO) based WBAN clustering algorithm to enhance energy efficiency, surpassing current state-of-the-art models. As the QLGWO uses quantum mechanics to enhance exploration and exploitation capabilities for optimization. It has improved convergence rates compared to other algorithms, can handle high-dimensional optimization problems, and is versatile and computationally efficient. It is especially useful for real-time optimization in applications such as WBAN energy optimization. The proposed algorithm is tested and evaluated for its energy efficiency, packet delivery ratio, and throughput in WBAN scenarios with varying numbers of nodes. The results demonstrate that the QLGWO-based algorithm outperforms existing state-of-the-art models, making it a promising approach for real-time energy optimization in WBAN applications.