Energy Management in Wireless Sensor Networks via a Hybrid Swarm Intelligence Based Clustering Algorithm

In this paper, a Multi-Objective swarm intelligence algorithm based on Firefly and Shuffled frog-leaping algorithms (named SFFA) is presented as an energy aware clustering protocol for Wireless Sensor Networks. The multi-objective fitness function of SFFA consider different criteria such as cluster heads’ distances from the sink, residual energy of nodes, inter- and intra-cluster distances, overlap and load of clusters, to select proper cluster heads at each round. The parameters of SFFA in clustering phase can be adaptively tuned to achieve the best performance based on the network requirements. Simulation outcomes demonstrate average lifetime improvements of up to 49.1% compared with LEACH, 38.3% compared with ERA, 7.1% compared with SIF and 11.3% compared with FSFLA in different network scenarios.