Evolutionary Hybrid Algorithms to Pioneer Localization in Wireless Sensor Networks

This research article delves deep into the intricate realm of wireless sensor network (WSN) localization, driven by the critical objective of minimizing localization errors. This pursuit of accuracy prompts the utilization of hybrid algorithms that seamlessly fuse Particle Swarm Optimization (PSO) + Firefly Optimization (FF) and Genetic Algorithm (GA) + FF. Drawing inspiration from nature-inspired computing paradigms, these hybrid algorithms represent a novel approach to addressing the localization challenge. By synergizing Particle Swarm Optimization (PSO) and Firefly Optimization (FF), this study pioneers a powerful solution that propels advancements in solving the intricate WSN localization problem. The convergence of these two algorithms brings forth substantial enhancements in accuracy, efficiency, and efficacy, reflecting the potential of such hybrid methodologies to revolutionize localization strategies. The research’s contribution lies not only in the successful application of these hybrid algorithms but also in their conceptual innovation, exemplifying the potency of nature-inspired approaches in the realm of computing. In a world increasingly reliant on accurate localization, the outcomes of this study hold implications for a wide array of applications, ranging from surveillance and environmental monitoring to healthcare. Through this exploration of evolutionary hybrid algorithms, this article not only addresses the immediate challenge of WSN localization but also lays the foundation for future advancements in the field.