Performance evaluation of CLPSO and MOPSO routing algorithms for optimized clustering in Vehicular Ad hoc Networks

Ad-hoc network is one of the eminent fields of wireless networks. MANETs and VANETs classify the Wireless ad-hoc network into two main categories. VANETs plays an essential part in the implementation of Intelligent Transportation System (ITS) to increase road safety based on Artificial Intelligence. The VANETs can be further classified in Vehicle to Infrastructure (V2I), Vehicle to Vehicle (V2V) and Hybrid (V2I & V2V). This research takes the advantage of V2V VANETs in order to improvise the vehicle to vehicle communication. The aim is to share traffic information, to avoid collision, efficiently manage the available resources and traffic situation accordingly. Various issues arise during the communication between vehicles and one of the solutions to those issues is clustering. This research work focuses on the clustering of vehicles and the communication is done from cluster head (CH) to CH. Various routing algorithms are present and available to be implemented to route information among vehicles in an efficient way to optimize the performance of the whole network. In this paper, bio inspired routing algorithms; Comprehensive learning PSO (CLPSO) and Multi-Objective PSO (MOPSO) have been compared on the basis of transmission range, number of clusters, number of nodes and grid size for the VANET environment. The results are shown from the comparison of both the algorithms.