Parallel multi-objective metaheuristics for smart communications in vehicular networks
This work provides an efficient framework for improving smart communications in vehicular networks, though it appears incremental as it applies existing metaheuristic methods to a specific domain.
The authors tackled the problem of optimizing routing protocol settings for vehicular networks using parallel multi-objective evolutionary and swarm intelligence algorithms, resulting in configurations that outperform state-of-the-art optimized ones with computational efficiency over 87%.
This article analyzes the use of two parallel multi-objective soft computing algorithms to automatically search for high-quality settings of the Ad hoc On Demand Vector routing protocol for vehicular networks. These methods are based on an evolutionary algorithm and on a swarm intelligence approach. The experimental analysis demonstrates that the configurations computed by our optimization algorithms outperform other state-of-the-art optimized ones. In turn, the computational efficiency achieved by all the parallel versions is greater than 87 %. Therefore, the line of work presented in this article represents an efficient framework to improve vehicular communications.