Gray Wolf Optimizer With Communication Strategy Based on DV-Hop for Nodes Location of Wireless Sensor Networks

Node localization remains a pivotal research challenge in Wireless Sensor Networks (WSN). The DV-Hop method, which estimates the positions of unknown nodes using anchor nodes and multi-hop forwarding, significantly lacks localization accuracy. To address this deficiency, this paper introduces an improved Grey Wolf Optimizer with a communication strategy (IGWO-CS). To alleviate the constraints imposed by the convergence factor in the conventional GWO algorithm, a Logistic chaotic mapping has been integrated. Moreover, two novel communication strategies, namely the Adaptive Weight Mechanism and Lévy Flight, have been implemented. The Adaptive Weight Mechanism dynamically adjusts positions during the weight update process, while the pronounced randomness and the mechanisms of Lévy Flight steps enhance information exchange among wolves, thereby improving convergence speed. Comprehensive experimental analyses on established benchmark functions have validated the efficacy of the IGWO-CS method, illustrating its competitiveness with other advanced algorithms such as the Ranking-based Adaptive Cuckoo Search (RACS) and Modified Particle Swarm Optimization (MPSO). The IGWO-CS method outperformed in 17 out of 23 benchmark functions. In simulation experiments, it achieved standard deviations of normalized errors and distance errors at 1.15E-04 and 2.31E-03, respectively. Quantitative evaluations confirm that the IGWO-CS method surpasses the RACS and MPSO in localization accuracy within WSN, demonstrating its substantial superiority.