Performance Comparison of Optimization Algorithms for Clustering in Wireless Sensor Networks

Clustering in wireless sensor networks (WSNs) is one of the techniques that can expand the lifetime of the whole network through data aggregation at the cluster head. This paper presents performance comparison between particle swarm optimization (PSO) and genetic algorithms (GA) with a new cost function that has the objective of simultaneously minimizing the intra-cluster distance and optimizing the energy consumption of the network. Furthermore, a comparison is made with the well known cluster-based protocols developed for WSNs, LEACH (low-energy adaptive clustering hierarchy) and LEACH-C, the later being an improved version of LEACH, as well as the traditional K-means clustering algorithm. Simulation results demonstrate that the proposed protocol using PSO algorithm has higher efficiency and can achieve better network lifetime and data delivery at the base station over its comparatives.