Optimal cluster head selection by hybridisation of firefly and grey wolf optimisation. (2018) - Murugan, T. Senthil; Sarkar, Amit
Clustering is one of the fundamental techniques for prolonging the life expectancy of Wireless Sensor Networks (WSNs). However, cluster head selection remains the major challenge in WSN concerning energy stabilisation. This paper intends to propose the Firefly Cyclic Grey Wolf Optimisation (FCGWO) to simulate the optimal cluster head selection framework. The main objective of this paper is to select the cluster head optimally by focusing on the stabilisation of energy, minimisation of distance between nodes and minimisation of delay. It hybridises the Firefly (FF) and Grey Wolf Optimisation (GWO) algorithms to attain the best performance. After the simulation, it compares the performance of the FCGWO-based cluster head selection with the traditional algorithms like Genetic Algorithm (GA), Group Search Optimisation (GSO), Artificial Bee Colony (ABC), Fractional Artificial Bee Colony (FABC), Firefly (FF) with Cyclic Randomisation (FCR) and GWO based cluster head selection. The performance comparison appears to analyse the network lifetime, energy efficiency and statistics of dead nodes. The simulation outcomes show that the proposed cluster head selection model is more efficient to prolong the lifetime of the network.