Energy Efficient Clustering in Wireless Sensor Networks Using Arithmetic Optimization Algorithm

Clustering plays a crucial role in maximizing the lifetime of Wireless Sensor Networks (WSNs) by reducing energy consumption and ensuring reliable data transmission. This study evaluates the Adaptive Optimization Algorithm (AOA) against four widely accepted clustering methods—Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and the LEACH protocol-based on key performance metrics, including energy efficiency, cluster quality, convergence time, scalability, network lifetime, and throughput. The results indicate that AOA outperforms existing methods across most metrics, making it a promising solution for resource-constrained WSNs. AOA demonstrates faster convergence than GA and ACO, superior cluster quality, and better load balancing compared to PSO. Additionally, a weighted performance score highlights AOA as the optimal choice for dynamic and large-scale WSNs, paving the way for advanced energy-efficient clustering techniques in next-generation networks.