Energy-efficient Clustering Based on Hybrid Evolutionary Algorithm in Wireless Sensor Network

Energy-aware algorithms are important factors for extending the lifetime of the wireless sensor network. In energy concerned fields, network clustering has proved to be an efficient technique that renders structures of low consumption. Yet, clustering protocols face a major issue that is of grouping sensor nodes in an optimal way. This is an NP-Hard problem which necessitates evolutionary algorithms in order to solve. In this paper, we explore a new hybrid optimization algorithm to decrease the energy consumption, in which modified particle swarm optimization and simulated annealing are combined to find the optimal clusters based on transmission distance. Simulated annealing is used as a local search around the best solutions of the modified particle swarm optimization. The simulation results show that our proposed protocol can improve the lifetime of systems compared with existing clustering protocols.