Optimizing Energy Efficiency in WSNs Using Hybrid Seagull-Whale Algorithm with LEACH Protocol

Abstract

Wireless Sensor Networks (WSNs) play a critical role in real-time monitoring tasks but are often limited by constrained energy supplies and inefficient routing methods. This study introduces an advanced hybrid clustering strategy that combines the Seagull Optimization Algorithm (SOA), Whale Optimization Algorithm (WOA), and the standard LEACH (Low-Energy Adaptive Clustering Hierarchy) protocol to enhance energy efficiency and prolong WSN lifetime. Although LEACH facilitates energy distribution by rotating Cluster Heads (CHs), it struggles with irregular CH placement and load imbalance. To address these challenges, the proposed LEACH-SOA-WOA approach selects CHs based on multiple factors such as residual energy, intra-cluster distance, and distance from the base station. SOA provides diverse exploration capabilities inspired by seagull migration, while WOA focuses on refining the search using its bubble-net strategy. Experimental results confirm that the hybrid method boosts energy savings, reduces isolated nodes, enhances cluster reliability, and extends both throughput and network life, outperforming traditional LEACH. This adaptive mechanism effectively maintains balanced CH selection in dynamic WSN environments.