An Evolutionary Optimization Based on Clustering Algorithm to Enhance VANET Communication Services

As a framework for facilitating intelligent communication between vehicles and enhancing the topic of interest pertaining to the safety and performance of traffic, vehicular ad hoc networks (VANETs) have developed. Effective communication among the vehicular nodes in VANETs is crucial due to the high vehicle mobility, varying number of vehicles, and dynamic inter-vehicle spacing. Hence, the evolutionary algorithm named Honey Badger Algorithm is used to improve communication in VANETs, which can successfully operate in high mobility node settings. HBA is built on an evolutionary algorithm with biological inspiration and a routing protocol based on game theory that dynamically adjusts to changes in network topology and distributes the load across network nodes through cluster formation as clustering improves network performance and scalability. Experimental comparisons of our approach with popular techniques like ant colony optimization (ACO), Hunger Game Search (HGS), Particle Swarm Optimization (PSO), and Firefly Optimization are made (FFO). The performance metrics Packet Delivery Ratio, Throughput, End-to-End Delay, Mean Routing Load, Control Packet Overhead, and energy used to assess the performance of communication services in VANETs. Experiments are carried out in MATLAB, and findings show that HBA delivers the best results for implementing vehicular services.