In this regard, optimizing vehicular networks for equal distribution of network load and scalability can be created by intelligent clustering algorithms that contribute key aspects. Also, in cases of surveillance and safety application, delay can be dangerous. Therefore, designing an appropriate routing protocol for vehicles in large-scale is a challenging issue. Lack of network scalability is a problem that leads to a lot of damage in the network sustainability. Moreover, for data to be transmitted efficiently, it is mandatory for efficient Quality of Service (QoS). In order to increase its lifetime, the mobility pattern or forecasting pattern can be anticipated, which will result in the extensive usage of application in multimedia, commercial, emergency, safety and managing of traffic applications. Eventually, this leads to network expiration resulting from the network separation. The VANET network communication includes Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) which are a dynamic network, being that there are nodes having inconsistent/random motion leading to nodes frequently experiencing structural deviations. The main objective of VRP is decreasing the cost of routes (such as time, distance, etc.) which are discovered in the progress of vehicular route discovery. Simulation results have confirmed the expected behaviour and show that our proposed protocols achieve better node connectivity and cluster stability than the former.Ī well-known combinatorial optimization problem is raised as the Vehicle Routing Problem (VRP) in transportation dialectics. The presented solution is verified and compared to classic Ant Colony Optimization (ACO), DFACO and ACO Based Clustering Algorithm for VANET (CACONET) algorithms in phase one and compared to clustering algorithms such as Center Position and Mobility CPM), Highest-Degree algorithm (HD), Angle-based Clustering Algorithm (ACA) in phase two through NS-2 and SUMO simulation tools. In this regard, major improvements are applied on classical DFACO by adjusting the procedure for updating the pheromone and tuning the evaporation rate that has a major role in DFACO. In this research two individual phases of experiments are conducted for performance evaluation of proposed routing protocols. In this study, we present five novel routing protocols based on Dynamic Flying Ant Colony Optimization (DFACO) algorithm to achieve minimum number of clusters, high accuracy, minimum time and solution cost by selecting the best cluster-head which is obtained from a new mechanism of dynamic metaheuristic-based clustering. One of the best solutions for such challenges is clustering. This study focuses on Vehicular Ad-hoc Networks (VANETs) stability in an environment that is dynamic which often leads to major challenges in VANETs, such as dynamic topology changes, shortest routing paths and also scalability.