# Enhanced Ant Colony Optimization for Vehicular Ad Hoc Networks Using Fittest Node Clustering
## 2 Literature Review  
Swarm intelligence represents a recent field that characterizes the behavior of distributed autonomous systems. Within the realm of vehicular networks, researchers investigate the communication and interaction between deployed vehicles and their environment through the lens of swarm intelligence [13]. This approach involves vehicles communicating directly, guided by factors such as architecture, speed, and light measurements. It mirrors the behaviors observed in natural phenomena like ant colonies, bird groups, fish schools, and microbial cognition. The challenges encountered during the transition phase of VANETs primarily revolve around ensuring stability and security in congested environments. Consequently, it is imperative to ensure connectivity in VANETs before practical deployment. The growing diversity within VANETs, with more vehicles equipped with on-board units (OBUs), enhances the likelihood of vehicular connectivity, thereby promoting fairness in vehicular communication modules [14]. Efficiently organizing reliable routing between deployed vehicular nodes is a task of great importance. Clustering emerges as an effective solution for designing efficient route planning processes and enhancing network stability. In the context of VANETs, clustering can be categorized into two classes: network associativity-based and identifier-based algorithms [2]. The former focuses on selected cluster heads (CHs) and members within their nearest transmission range, while the latter operates in a manner similar to the CH with the least ID. In a study, it was noted that the connectivity-based model tends to perform less optimally than identifier-based algorithms [6]. In line with this observation, the CBRP algorithm was introduced, which optimizes the route planning process by transforming the lowest identifiers [15]. An ant colony system (ACS) was utilized for routing performance analysis [16]. Both the ACS and the ACS's variation algorithms have redefined challenges associated with clustering. However, the ACS's speed and accuracy have shown limitations. To address these limitations, an colony optimization with different favors (ACODF) combines the selection of favorable ants with the stimulated annealing (SA) strategy, resulting in a faster path selection process [17]. This approach calculates node intensities, selects nearby nodes as ants, and utilizes a reactivation function based on pheromones. The cluster formation takes place at the edge points of the ant data. The ACS and its variation algorithm were applied to cluster data [18]. Similar studies, such as ant K-means (AK) and ant systems (ASCA), were conducted to address challenges related to clustering systems [19]. The combination of AK and ASCA was introduced to enhance the performance of database clustering models, with a focus on association rules algorithms and the continuous updating of pheromones using variance [20]. Moreover, trust-based ant colony routing (TACR) has been explored, considering parameters like vehicular nodes' position, direction, and relative speed [21]. To enhance the scalability of the clustering process, real-time updates of vehicle position and trust are taken into account. TACR was compared to the mobility aware-based DYMO algorithm, revealing the need to improve routing overhead in TACR compared to MAR-DYMO [22]. Conventional clustering architecture and ACO are employed to enhance the efficiency of routing operations, making ACO deployment beneficial for the VANET environment. The advancement of VANET technologies has been utilizing the 5G network architecture for better network stability and connectivity. One study presented the software-defined network (SDN) on 5G communication systems to administer urban traffic [30]. The intention was to enhance repercussion time under the emergency scenario. A few parameters, such as vehicle localization, traffic forecasting, and control, were carried out by intelligent architectural design.