### Background Research and Literature  
Metaphor and natural metaheuristics are two examples of bio-inspired approaches that can be used to solve the NP-hard clustering optimization problems of in-vehicle networks [16]. In contrast to conventional metaheuristics, biologically inspired approaches or metaheuristics procedures are error-free. The techniques here take their cues from real-world examples, be they biological, natural, or human-made. These techniques are used to address a wide range of NP-hard optimization problems, and they do not require any prior knowledge of the problem domain. When combined with efficient search strategies, metaheuristics can quickly identify the optimal answer. It has been established that routing in a VANET is an NP-hard problem. Clustering algorithms form an integral part of several CBR methods [17]. Multi-Objective Problems (MOPs) are put into practice in the field of clustering [18]. In particular, these MOPs affect routing in ad hoc networks. The effectiveness of traditional QoS is affected by several variables. These include Packet Delivery Ratio (PDR), average end-to-end delay, and bandwidth utilization. Some of the benefits of clustering optimizations, as shown in Figure 1, include network topology stability, data aggregation, minimizing the number of clusters, bandwidth optimization, and efficient handover management [19]. As cluster stability increases, the need for these optimization tasks decreases [20]. However, metaheuristics, particularly bio-inspired optimization techniques, can be used to increase cluster performance even though cluster stability is an NP-hard problem. Figure 2[21] as shown below depicts the overall structure of VANETs. Figure 1: Cluster Optimization Advantages in VANETs [19]. Figure 2: Clustering in VANETs [21]. This is seen in [22] when [PERSON]'s expedition to free the Ant Lion Optimizer from local optima displaces the insects' shambolic gait. To further strengthen the presentation of ALO, the population growth rate was used as an input to progressively adjust the deception dimensions by the \(1/5\) Principle, which includes changes to assembly precision, velocity, and control. The authors of [23] suggest implementing the lion optimization algorithm (LOA) in VANETs. It is a refined method of routing in automobile networks that use LOA QoS. Utilizing mobility from local to stronger networks, this technique enhances vehicle QoS pathfinding, which is inspired by a lion pride's most important characteristics. The authors of [24] introduced the Bull Optimization Algorithm to facilitate routing based on reactive topologies. The Bull Optimization Algorithm is a property of all forms of directional finding that, when given a sufficient evaluation constraint, interferes with the production of optimal pathways for recovery and regular forwarding. One method for routing in VANETs that was proposed in [25] was based on water wave optimization (WWO). The optimal path was determined by simulating water-wave characteristics and taking into account QoS requirements, collision probabilities, and network congestion. According to [26], a genetic algorithm-enhanced method based on the AODV was proposed (G-AODV). It was a safe method of backup routing that is used only when necessary, and it helped strengthen the security of the connections between nodes and other networks. To improve VANET multicast routing performance, [27] recommended emulating bee-life behavior (BLA). It was presented to address the NP-complete Quality-of-Service Multicast Routing Problem (QoS-MRP) in VANETs. Using preexisting paths that maximize bandwidth utilization while minimizing cost and time, the ABC Algorithm determined the optimal multicast tree between the sender and the receiver. The BLA took cues from bee activities such as breeding and foraging to generate new routes predicated on the BLA's tolerance of scavenging in the surrounding area. The ACO-based clustering technique presented in [28] was a novel CACONET (Constrained Ad Hoc Network) algorithm.