### Background Research and Literature
This algorithm addressed the issue of VANET scalability. The CACONET minimized cluster size while maintaining CH stability, which resulted in less strain on the network's infrastructure. The authors of [29] presented a moth-flame-based clustering algorithm called CAMONET. This approach utilized both-Flame Optimization (MFO) to guarantee a high cluster lifetime and an ideal CH number, two crucial components of a reliable network. This concept was inspired by the moths' ability to track their flight path by watching the moonlight as they travel at night. A moth keeps an eye out for the flaming object in space and reports its whereabouts. By determining the CNs' speeds, directions, and communication ranges, this phenomenon made it possible to trap CHs and reduce the network's cluster density while simultaneously taking advantage of the moth's position and intended capabilities to improve location via a diminishing aspect. Consequently, the most effective clusters for dependable networking were amassed. When using a wide range of densities and transmission distances, CAMONET outperformed CACONET. Node clustering in VANET, based on the work of [PERSON], was presented in [21] (GWOCNET). GWOCNET made use of the hunting and social habits of grey wolves to find the optimal cluster size. A vehicle's heading, velocity, and location can all be determined with a cluster number, which must be optimized. The GWOCNET model minimized the linear factor convergence discovered during various phases of wolf hunting by using methods such as social ranking for hunting guidance, seeking out prey, highlighting prey, and attacking prey. Increasing the quality of all optimization methods, including the selection of the alpha wolf, requires the development of the election of the leader wolf. The suggested method built the optimal number of clusters across all zones and communication distances, outperforming both CLPSO and MOPSO [30]. For VANETs, the authors of [31] took cues from fireflies and suggested a multi-objective weighted clustering method (RWCP-MFO). This algorithm used the metaheuristics of the Firefly Algorithm, which was inspired by the fluttering movements and light-intensity observations of fireflies, to optimize the RWCP parameters while accounting for the vehicles' speeds, directions, reputations, land identifiers, and neighborhood sizes. Multiple agents gathered information at a secure urban monitoring site, as proposed in [32] (Datataxis). When E. coli infiltrated a network, it disrupted a topology-based unicast routing protocol. According to [33], glow-worm routing packets were propagated via glow-worm swarm optimization (GSO) to provide numerous routing paths. The GSO approach made use of new phenomena of required data that were node-specific. The ED between the current hop and the source, as well as the number of cars present, were what established the fitness value. Following a calculation of the fitness value, the luciferin was optimized with each successive hop. By utilizing inter-layer approaches, a traffic flow system, and an AI-based system for cluster selection that took into account cluster size, network density, and CN velocity, the authors of [34] presented an intelligent-based clustering algorithm in VANETs (IBCAV) that enhanced directional accuracy. A new clustering strategy for VANETs was created in [35]: a highway-transmittable environment. To establish reliable groups, the authors suggested an algorithm based on the Ant Colony System (ACS) called ASVANET. By taking into account travel time, road quality, and mobility congestion, the authors of [36] proposed an ACO- and PSO-based synchronized self-motivated direction-finding optimization strategy to aid the central decision-making routing system. These evaluations showed how effective PSO and ACO algorithms are at cutting down on travel time.