Energy-Efficient Bi-Objective Optimization Based on the Moth–Flame Algorithm for Cluster Head Selection in a Wireless Sensor Network

Designing an efficient wireless sensor network (WSN) system is considered a challenging problem due to the limited energy supply per node. In this paper, performance of several bi-objective optimization algorithms in providing energy-efficient clustering solutions that can extend lifetime nodes were investigated. Specifically, we use Moth–Flame Optimization (MFO) algorithm and Salp Swarm Algorithm (SSA), as well Whale (WOA), cluster-head selection decisions. Compared reference scheme using Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol, simulation results showed integrating MFO, SSA or WOA into WSN protocols could significantly lifetime, which improved nodes’ residual energy, number alive nodes, fitness function throughput. The also revealed MFO outperformed other terms efficiency.