A hybrid fennec fox and sand cat optimization algorithm for clustering scheme in VANETs

Abstract

The popularity of intelligent vehicles with cutting-edge vehicular applications has fueled the rapid expansion of Vehicular Ad hoc Networks (VANETs) in recent years. VANETs are a network of vehicles designed to exchange and explore real-time data using a well-developed and effectively organized data transport technology. However, the major issue of dynamic topology and cluster stability always has an impact on choosing an optimal path between the cars. At this point, an intelligent clustering technique in VANETs that handles dynamic topology and cluster stability is critical for efficient route selection between vehicular nodes. This is an NP-hard issue that can be effectively solved using an intelligent nature-inspired algorithm that can discover near-optimal solutions in the search space. An Intelligent Hybrid Fennec Fox and Sand Cat Optimization Algorithm (HFFSCOA) -Based Clustering Scheme is proposed in this paper as a novel route clustering optimization strategy that takes grid size, orientation, velocity node density, and communication range into account while achieving its goal. This HFFSCOA contributed to the route clustering process, which determines dependable and optimal routes between vehicular nodes for the purpose of building and evaluating ideal Cluster Heads (CHs) in the network. HFFSCOA's findings clearly demonstrated its usefulness and efficacy in terms of the number of vehicles, network size, changeable communication ranges, and number of clusters built in the network. The statistical results of HFFSCOA also confirmed an enhanced cluster Optimization rate of 56.21% and an increased cluster stability of 92.34.