Parametric Analysis of the Nature-Inspired Algorithms based on VANETs

Because of their ability to efficiently communicate in fast-changing conditions, Vehicular Ad-Hoc Networks (VANETs) have become a focus for research. Unlike MANETs, VANETs are subject to disconnects, high-speed movement and quick changes in their network structure which creates many challenges when designing and optimizing protocols and resources. Emergency alerts, collision avoidance, managing traffic jams and cooperative driving all depend on highly trustworthy communication routes. To deal with these issues, Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and the Bacterial Foraging Optimization Algorithm (BFOA) have been used, helping find the best way to go. Bringing together Cuckoo Search Algorithm (CSA) and Differential Evolution (DE) further improves how efficiently the search is performed. These algorithms are analyzed using NS2, considering packet delivery ratio, throughput, the amount of overhead in routing, delay and packet loss rate. Results from experiments demonstrate that bio-inspired methods work more efficiently and reliably than traditional methods.