Clustering strategy of Wireless Sensor Networks based on improved Discrete Particle Swarm Optimization

In this paper, an improved Discrete Particle Swarm Optimization (DPSO) algorithm is proposed to solve the uneven clustering problem, a difficult problem in Wireless Sensor Networks (WSNs) area. An uneven clustering result leads to severe energy imbalance in WSNs, which shortens the life time of networks. This paper presents a strategy to obtain good clustering result by directly using an improved DPSO algorithm. The phenomenon of premature stagnation of elementary Particle Swarm Optimization (PSO) is avoided by proposing mutant strategy and mixed dynamic inertia weight adjustment strategy, heuristic algorithms are also presented to help find global best solution efficiently in this paper. Remaining energy of cluster heads (CHs) is taken into consideration in the process of clustering too. Experiments demonstrate the proposed algorithm obtains much better clustering result in comparison with other classic clustering strategies such as Low Energy Adaptive Clustering Hierarchy (LEACH) and its improved strategy.