Hybrid Flying Foxes and Enhanced Whale Algorithm‐Based Cluster Optimization Method for Efficient Stable Routing in Vehicular Ad Hoc Networks (VANETs)

ABSTRACT Vehicular ad hoc network (VANET) is an indispensable entity to diversified number of intelligent transportation system (ITS)–enabled technologies. But network scalability, frequent topology changes, and high mobility are the major problems due to the sparse distribution of vehicles especially in highways and constantly changing vehicular network topology. Maintenance of stable route in the network between the vehicles is a herculean task as its failure increases the probability of instability. This establishment of stable routes is essential in VANETs for efficiently utilizing the computational resources such that desirable degree of quality of service (QoS) can be achieved. This stable route determination can be attained by addressing the factors of energy balancing, coverage, connectivity, and load balancing for the purpose of guaranteeing the sensed data from all the points of target to the base stations in a reliable manner. In this paper, hybrid flying foxes and enhanced whale algorithm (HFFEWA)–based cluster optimization method is proposed for attaining sustained routing that establishes stable cluster construction during the routing process. This HFFEWA adopted the factors of route along the highway, velocity, number of nodes, and communication range into the fitness function for minimizing the degree of randomness. It specifically used flying fox optimization algorithm (FFOA) for exploring the search space more eminently such that global clusters could be constructed with maximized diversity. On the other hand, enhanced whale algorithm (EWA) is adopted for preventing the issue of premature convergence. It is also proposed with the capability of well‐balanced exploration and exploitation that explores and exploits the search space such that it can be used in generating optimal number of cluster heads (CHs). The simulation results of this HFFEWA conducted different vehicular density confirmed an improved network lifetime of 19.42% with the stabilized cluster construction of 32.18%, better than the competitive approaches. The evaluation of HFFEWA under different network size confirmed better performance in packet delivery rate, end‐to‐end delay, and packet loss.