Clustering Approach Based Hybrid Optimization Using Grey Wolf and Moth-Flame Optimization (MFO) in Autonomous Electrical Vehicles

The article focuses on optimizing clustering in Autonomous Electrical Vehicles (AEVs) using a hybrid optimization method that combines GWO and MFO. It outlines various sections, including related studies, the proposed CHO-GWMF model, research experiments, and conclusions. The model aims to enhance communication in the VANETs by refining clustering algorithms. Key terms such as security issues and intelligent transportation systems are discussed. Comprehensive simulations showcase the CHO-GWMF model's superior performance in metrics like throughput and average delay, underscoring its effectiveness in improving VANET reliability.