An efficient hybrid model for cluster head selection to optimize wireless sensor network using simulated annealing algorithm

Objective: Energy efficiency aspect in wireless sensor networks (WSN) can be achieved by small sized rechargeable and easily replaceable batteries. The lifetime of network improved identifying the efficient reliable nodes as a cluster heads using Hybrid Simulated Annealing algorithm. proposed algorithm identifies head to reduce overhead is capable handling high volume with minimum node death rate. Methods: This study initialization population vectors opposite point procedure, self-adaptive control approach mutation rate, crossover capacity allocation Methods. Findings: A case work found better throughput, accuracy, efficiency, energy utilization, batteries recharge ability replacement procedures compared conventional methods. By analysis comparison method existing methods, it identified that reduction number dead gradually increases throughput respect iterations. Novelty: To overcome limitations Low Adaptive Clustering Hierarchy (LEACH), harmony search (HSA), modified HSA differential evolution, we propose hybrid optimal model simulated annealing which includes capability function. It used improve nodes. have option reliability network. Keywords: Wireless network; Differential Evolution; Hierarchy; HAS;