Hybrid artificial bee colony and glow worm algorithm for energy efficient cluster head selection in wireless sensor networks

Abstract

Purpose

This paper aim to find optimal cluster head and minimize energy wastage in WSNs. Wireless sensor networks (WSNs) have low power sensor nodes that quickly lose energy. Energy efficiency is most important factor in WSNs, as they incorporate limited sized batteries that would not be recharged or replaced. The energy possessed by the sensor nodes must be optimally used so as to increase the lifespan. The research is proposing hybrid artificial bee colony and glowworm swarm optimization [Hybrid artificial bee colony and glowworm swarm optimization (HABC-GSO)] algorithm to select the cluster heads. Previous research has considered fitness-based glowworm swarm with Fruitfly (FGF) algorithm, but existing research was limited to maximizing network lifetime and energy efficiency.

Design/methodology/approach

The proposed HABC-GSO algorithm selects global optima and improves convergence ratio. It also performs optimal cluster head selection by balancing between exploitation and exploration phases. The simulation is performed in MATLAB.

Findings

The HABC-GSO performance is evaluated with existing algorithms such as particle swarm optimization, GSO, Cuckoo Search, Group Search Ant Lion with Levy Flight, Fruitfly Optimization algorithm and grasshopper optimization algorithm, a new FGF in the terms of alive nodes, normalized energy, cluster head distance and delay.

Originality/value

This research work is original.

Citation

Kaur, J., Rani, P. and Dahiya, B.P. (2022), "Hybrid artificial bee colony and glow worm algorithm for energy efficient cluster head selection in wireless sensor networks", World Journal of Engineering, Vol. 19 No. 2, pp. 147-156. https://doi.org/10.1108/WJE-03-2021-0170