###### Abstract
Vehicle interconnections aim to provide seamless communication between vehicles on the road and internet-connected infrastructure, enhancing the efficiency, safety, and intelligence of urban transportation systems. In the context of Smart Cities, vehicle interconnection enables real-time vehicle interconnection, allowing for more efficient traffic management, improved road safety, and enhanced user experiences through intelligent services [13]. A typical vehicle interconnection network is composed of On-Board Units (OBUs) carried by vehicles and Road-Side Units (RSUs) that provide connectivity to the internet infrastructure, forming a backbone for smart mobility solutions. Essentially, vehicle interconnection architecture is described through three key aspects: communication between its elements, the protocol stack, and the topological scenario [14]. These architectures are fundamental to Smart Cities as they facilitate vehicle-to-everything (V2X) communication, ensuring that vehicles can interact with other vehicles, infrastructure, and even pedestrians, contributing to safer and more connected urban environments. The communication between OBUs and RSUs in Smart Cities can be formed in three VANET architectures: Vehicle-to-Vehicle (V2V) communication, enabling vehicles to share critical safety and traffic information [15]. Vehicle-to-Infrastructure (V2I) communication, providing vehicles with access to traffic signals, parking availability, and road conditions. Hybrid communication, combining V2V and V2I for more comprehensive coverage and dividing the architecture of vehicle interconnection into three domains, are shown in Figure 1: ad hoc domain, in-vehicle domain, infrastructure domain. These domains serve as the foundation for building Smart Cityecosystems, where intelligent transportation systems (ITS) leverage VANETs to enhance urban living, reduce congestion, and minimize environmental impacts. The system engages in the optimization of routing protocols for multiple vehicle connection requirements and identifies the impact of multi-connection scenarios on the optimal solution. It also evaluates the robustness of routing protocols in complex topological variations, while maintaining a certain level of performance across different scenarios to achieve the best throughput. The study proposed two optimization techniques: Taguchi and Differential Evolution methods. The Taguchi method has been benchmarked in various studies for parameter tuning and optimization in communication networks. For example, in [16; 17], Taguchi was used to optimize routing protocols for wireless sensor networks (WSNs), showing that it can significantly improve network performance with fewer experiments compared to traditional optimization methods [18]. This aligns with the efficiency needs of VANETs, where rapid adaptation to changing conditions is required [19]. On the other hand, Differential Evolution (DE) has been extensively benchmarked in evolutionary algorithms and applied in optimization problems, particularly where the search space is large and non-linear. In [20], DE was applied to traffic management and network routing in VANETs, where it outperformed other methods like genetic algorithms in terms of convergence speed and solution accuracy. This makes DE an ideal choice for optimizing VANET protocols, as it can effectively handle complex, multi-dimensional search spaces, such as those involving packet delivery ratio, delay, and throughput. Both TOM and DEM were chosen not only for their demonstrated success in similar optimization tasks but also for their ability to provide adaptable solutions under varying conditions, such as city and highway scenarios in VANETs. By benchmarking these methods against alternative techniques, we ensure that our results are both robust and representative of the best possible optimization strategies available in the literature.