# Enhanced Ant Colony Optimization for Vehicular Ad Hoc Networks Using Fittest Node Clustering
## 2 Literature Review
An Internet of Vehicles (IoV)-based optimizer algorithm was introduced to support a dynamic routing system [31]. Adopting particle swarm optimization (PSO) and ACO for the routing development process proved that ACO has better efficacy than PSO in minimizing traveling time. A contact centric-based network (CCN) was introduced to eliminate the scarcity of IPs during transmission [32]. The concept of CCN-IoV in the CCN methodology was studied [33]. The performance of network delay and throughput parameters was not remarkable. The scalability of the IoV needs to be improved. Two metaheuristics-based algorithms also investigate the exploration of network performance using ACO in a VANET, comprehensive learning oriented PSO, and multi-objective PSO [34]. The study explored cost maximization for packet routing and minimized usage of cluster counts. However, the weights are static. In alignment with this, other parameters such as transmission range, mobility model, and topology are considered static. To summarize, the dynamic nature of evolutionary techniques can unleash interesting solutions to support large-scale networks. To further enhance the MOPs, the dynamic characterization of evolutionary algorithms have been incorporated. Several baseline algorithms, such as artificial immune system (AIS) [35], adaptive neuro-fuzzy interface system (ANFIS) [36], and genetic algorithm (GA) have been discovered [37]. Reinforcement learning maintains its effectiveness over time in finding the most optimal route in the VANET network [38]. An adaptive beaconing strategy based on the fuzzy logic scheme for geographical routing was investigated which effectively reduced the routing overhead and improved the packet-delivery ratio, throughput, average end-to-end delay, and normalized routing load as compared to traditional routing protocols [39]. ACO has resolved various optimization problems by proving its ability in solution discovery, better search strategies, and robustness. However, a few drawbacks are maximized, such as the time taken for searching, convergence speed, and stagnation of discovered solutions. The design of improved ACO will meet the following scenarios. 1. Evaluating the communication stability under a dynamic nature. 2. Estimating the fairness in network load distribution to have an optimized and scalable VANET environment. 3. Evaluating the performance regarding packet delivery, throughput, and network delay to the number of vehicles and time. \begin{table}
\begin{tabular}{c c c c} \hline
**Reference** & **Routing Application** & **Clustering Protocol** & **Performance Evaluation** \\ \hline [23] & Routing optimization & While algorithm & PDR, throughput, and overhead \\ \hline [24] & True Pareto optimal & Dragonfly algorithm & PDR, throughput, and delay \\ \hline [25] & Disconnections during communication & Firefly algorithm & Coverage \\ \hline [26] & Capacitated vehicle routing problem & Grey wolf algorithm & PDR, throughput, delay, and degree of node \\ \hline [27] & Rapid modulation in topology &
\begin{tabular}{c} Fuzzy bacterial foraging \\ optimization \\ \end{tabular} & No. of clusters and grid size \\ \hline [28] & Clustering formation & ACO and PCO algorithm & Simulation time and radio propagation \\ \hline [29] & Geocast routing & PSO optimized & PDR, normalized routing load, and throughput \\ \hline \end{tabular}
\end{table} 
Table 1: Literatures of state-of-art clustering techniques in VANET applications.