Performance Assessment of Particle Swarm Optimization with Grey Wolf Optimization Algorithms for Clustering in Wireless Sensor Networks

Wireless Sensor Network (WSN) comprises of numerous cheap and low power operated sensor nodes. The sensor nodes are randomly deployed in harsh environment which is highly energy constrained in nature. Clustering is an energy efficient technique which groups the nodes into a number of clusters. Each cluster holds a separate Cluster Head (CH) where the data will be transmitted to other nodes present in the cluster. The major issue in the clustering process is the identification of proper CHs with respect to several constraints like energy, distance, node degree and so on. In recent days, several bio-inspired optimization algorithms have been presented to select the CHs properly. This paper assesses the performance of two optimization algorithms namely Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) algorithms based clustering techniques in WSN. The compared PSO and GWO algorithm has been simulated under a network of 100 sensor nodes in the target area of 100x100m².The investigation of the outcome of these algorithms takes place under several aspects.