An Energy Efficient Wireless Sensor Network for Optimal Routing using Hybridized Bio-Inspired Technique: An Integration of Modified Harmony Search and Competitive Swarm Optimization Algorithm

Due to their spatial distribution and energy constraints, the efficiency of Wireless Sensor Networks (WSNs) relies heavily on effective energy management. Optimizing energy consumption can significantly enhance network longevity and performance. While clustering techniques, such as Low Energy Adaptive Clustering Hierarchy (LEACH), help reduce energy usage, they suffer from inefficient local searches and poor exploration-exploitation balance.To address these limitations, this study proposes a hybrid bio-inspired optimization technique—Modified Harmony Search Algorithm (MHSA) combined with Competitive Swarm Optimization (CSO)—for optimal cluster head (CH) selection. The MHSA enhances global search efficiency, while CSO dynamically adapts to network changes, leading to improved convergence rates and balanced energy distribution.Performance evaluation, based on key metrics such as the proportion of alive nodes, residual energy, and throughput, demonstrates that the MHSA-CSO hybrid significantly outperforms existing WSN approaches. The proposed method achieves latency reductions by more than three orders of magnitude and energy efficiency improvements exceeding two orders of magnitude, effectively extending the operational lifetime of WSNs. This approach offers a robust, energy-efficient routing solution for WSNs, contributing to more sustainable and resilient network designs.