A Parallel Compact Phasmatodea Population Evolution Algorithm for Energy Efficient Optimal Cluster in Wireless Sensor Networks

Abstract

In wireless sensor networks, the sensor nodes are powered by batteries and hence the energy is limited. This paper presents an algorithm -- Parallel Compact Phasmatodea Population Evolution (PCPPE), and then Differential Evolution (DE) is incorporated into the PCPPE algorithm, where Phasmatodea Population Evolution (PPE) is a meta-heuristic algorithm that incorporates the characteristics of the natural evolution of the Phasmatodea populations. We compare the proposed algorithm with PPE and five other algorithms on the test set of CEC2013, and the experimental results show that the PCPPE algorithm obtained better results. Finally, the proposed PCPPE algorithm is applied to select the best cluster head for wireless sensor networks (WSN) so as to reduce the energy consumption for WSN. The fitness function considers the average intra-cluster distance, the distance from the cluster head to the base station and the residual energy of the cluster head nodes. Subsequently, the proposed algorithm is compared with LEACH, PSO-C, HFAPSO, and PCFMO. The experimental results show that the proposed algorithm preserves the cluster head’s energy consumption more significantly and effectively improves the network lifetime of WSN.