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.

翻译：本文分析了两种并行多目标软计算算法在车载网络中自动搜索Ad hoc On Demand Vector路由协议高质量参数配置的应用。这些方法分别基于进化算法与群体智能方法。实验分析表明，通过我们的优化算法计算得到的配置方案优于其他现有最先进的优化方案。与此同时，所有并行版本实现的计算效率均超过87%。因此，本文提出的研究路线为改进车载网络通信提供了一个高效框架。