## 2 Related Works  
### Optimization-Based Approaches  
Ref. [13] created a new routing protocol with the combination of an Ad-Hoc On-Demand Vector (AODV) and Ant Colony Optimization (ACO) technique to improve the concept of path selection. Hence, VANETs are highly dynamic in nature; it is essential to optimize the network, which helps to reduce the congestion in the network and also to reduce the consumption of energy. This method generates a path with the shortest distance that results in high throughput, low packet loss, and delay. However, it fails to reduce the routing overhead, and it is not suitable for a network with huge mobility.  
Ref. [14] introduced Vehicular Ad Hoc Networks (VANETs) with Reliability Aware Multi-Objective Optimization Based VANETs Routing (RAMO), which is combined with Enhanced Gaussian Mutation Harmony Searching (EGMHS). The process behind this search is Gaussian mutation, objective decomposition, and a harmony memory extraction algorithm. This method helps to increase the parameters such as delay and packet delivery ratio. The drawback is that the throughput of the network is not concentrated.  
Ref. [15] presented a model to address the One-Dimensional Roadside unit Deployment (DIRD) problem with the help of a greedy approach called Greedy2P3E. This method helps to achieve better performance in real vehicle trajectories. The achieved result is moderate. Ref. [16] created a Deep Independent Q-Learning model to reduce the delay in Unmanned Aerial Vehicles (UAVs). The concept behind this model is multi-agent reinforcement learning (MARL). This method helps to reduce the complexity of the highway models. The overall performance is moderate. The communication type of UAV shows in Figure 2.  
Ref. [17] proposed two discretized variants of cuckoo search optimization (CSO) to improve the routing performance in VANETs. The two variants are the Levy flight-based discrete variant and the random walk-based discrete variant. High route reliability is achieved using this method. This method is suitable for networks with low vehicle density scenarios.  
Ref. [18] presented a new approach to improve the optimization of routing. The drawbacks in traditional protocols such as AODV, OLSR, and DSDV are analyzed. To overcome that, the Hybrid Genetic Firefly Algorithm-based Routing Protocol (HGFA) is introduced. This method achieves better performance when compared with traditional optimization techniques such as PSO and ACO.  
Figure 2: UAV Communication.  
Ref. [19] proposed a model to secure the network from traffic congestion, and roadside accidents called the Multi-Objective Harris Hawks Optimization (2 HMO-HHO) algorithm. It is based on a 2-Hop routing algorithm. The parameters that are improved by using this method are delay, packet delivery ratio, throughput, and overhead. This method is suitable for the network with low vehicle density scenarios, not for densely populated areas.  
Ref. [20] introduced a method to reduce the end-to-end delay of the network called a hybrid-fuzzy logic-guided genetic algorithm (H-FLGA) approach. By the use of this method, the network flexibility and scalability are increased, which leads to minimizing the value of end-to-end delay. However, this method fails to concentrate on packet delivery ratio and throughput.