## 1 Introduction
### Background and Motivation  
In the realm of modern transportation, the landscape is rapidly evolving, presenting a complex challenge for traditional routing methods initially tailored to conventional vehicles. This complexity is particularly evident in emerging transportation systems like EnFVs [9]. Within the spectrum of routing solutions, there is a notable limitation. The majority of existing approaches tend to focus narrowly on a single objective, often at the expense of other critical considerations. However, there is a promising subset of solutions that adopt a rigorous analytical approach. The solutions leverage mathematical calculations to identify optimal routes while simultaneously considering multiple objectives [5]. While this approach demonstrates potential in specific scenarios, it may lack the requisite dynamism and adaptability for real-world applications, occasionally resulting in excessively convoluted routing.  
Furthermore, it is important to acknowledge a scarcity of research addressing the domain of multi-objective routing within the context of EnFVs [2]. This research gap underscores the pressing need for innovative and versatile routing strategies tailored to the unique demands of EnFVs. These demands include a wide range of factors, from energy efficiency and real-time adaptability to navigating complex and ever-changing environments. But let us delve deeper. Picture this: EnFVs, electric vehicles (EVs), and flying vehicles (FVs) like drones are revolutionizing transportation. Ensuring efficient message routing in these vehicles is absolutely critical for their seamless operation within dynamic transportation systems. In the realm of EVs, message routing optimization is key. We are talking about keeping the information flow reliable and real-time while considering factors like energy usage, where to find charging stations, and staying within the vehicle's range [10]. Now, for FVs such as drones and UAVs, it is a whole new ballgame. Message routing algorithms must factor in flight restrictions, airspace regulations, and the ever-changing landscape of obstacles.  
The exciting part? When we optimize message routing, we are not just improving a single aspect; we are supercharging the performance and functionality of EnFVs. Think intelligent transportation systems, futuristic package deliveries, and high-tech surveillance operations [3]. We must admit, in the world of high-mobility vehicles with multiple requirements, finding the absolute best route is quite the challenge. But here is the thing:there is often a solution that comes really close to meeting all the requirements and, honestly, beats the competition. After a deep dive into the literature, it is clear that genetic algorithms (GAs) are a popular choice for routing strategies. GAs excel in optimization due to their ability to perform global searches, robustness in complex and noisy environments, and adaptability to various problem types. They do not require derivatives, can be parallelized for efficiency, and balance exploration and exploitation effectively. GAs maintain diverse solution populations, facilitate crossover to produce better solutions, and are heuristic, making them versatile for real-world problems. However, their effectiveness depends on problem characteristics, and the choice of optimization technique should be problem-specific. However, there is a twist--there is surprisingly little literature on their use in EnFVs communications and routing.