## 7 Future Research Directions
### Addressing Scalability and Large-Scale Routing Problems  
One of the key challenges in multi-objective message routing in EnFVs is scalability, especially when dealing with large-scale transportation networks [91]. As the number of vehicles and communication nodes increases, the complexity of the routing problem grows exponentially, making it computationally expensive and time-consuming to find optimal or near-optimal solutions. Therefore, addressing scalability is crucial to enable the practical implementation of GA-based routing approaches in real-world scenarios. To tackle scalability, future research should focus on developing efficient and scalable algorithms for large-scale routing problems. One potential direction is the exploration of parallel and distributed computing techniques. By leveraging the power of parallel processing and distributed computing architectures, it is possible to distribute the computation load among multiple computing units, enabling faster and more scalable solutions. For instance, techniques such as parallel GAs, map-reduce frameworks, and cloud computing infrastructure can be explored to efficiently tackle large-scale routing problems [1; 49]. Moreover, the utilization of metaheuristic approaches beyond GAs could be investigated. Evolutionary algorithms, including genetic algorithms, have shown promise in multi-objective optimization, but other metaheuristic algorithms such as particle swarm optimization, ant colony optimization, or simulated annealing can also be considered. These algorithms offer different search strategies and exploration-exploitation trade-offs, which may provide improved scalability and convergence properties for large-scale routing optimization problems [91].  
Another avenue for future research is the development of hybrid approaches that combine GAs with other optimization techniques or heuristics [3; 38]. Hybrid algorithms can leverage the strengths of different algorithms to address scalability challenges effectively. For example, integrating GAs with local search methods or metaheuristic hybrids could potentially improve the exploration of the solution space and enhance the convergence speed for large-scale routing problems [7; 91]. Additionally, the investigation of approximation algorithms and heuristics tailored specifically for large-scale routing problems is warranted. These algorithms aim to provide near-optimal solutions within reasonable computational time, even though they do not guarantee global optimality [92]. Approaches such as local search, greedy algorithms, or clustering-based heuristics can be explored and customized to address the unique characteristics and constraints of EnFV routing scenarios.  
In conclusion, future research should prioritize addressing scalability challenges and focusing on large-scale routing problems in the context of multi-objective message routing in EnFVs. Exploring parallel and distributed computing techniques, considering alternative metaheuristic algorithms, developing hybrid approaches, and investigating approximation algorithms and heuristics will contribute to the practical implementation of efficient routing solutions for real-world transportation networks.