Recent advances in wireless technologies have given rise to the emergence of vehicular ad hoc networks (VANETs). In such networks, the limited coverage of WiFi and the high mobility of the nodes generate frequent topology changes and network fragmentations. For these reasons, and taking into account that there is no central manager entity, routing packets through the network is a challenging task. Therefore, offering an efficient routing strategy is crucial to the deployment of VANETs. This paper deals with the optimal parameter setting of the optimized link state routing (OLSR), which is a well-known mobile ad hoc network routing protocol, by defining an optimization problem. This way, a series of representative metaheuristic algorithms (particle swarm optimization, differential evolution, genetic algorithm, and simulated annealing) are studied in this paper to find automatically optimal configurations of this routing protocol. In addition, a set of realistic VANET scenarios (based in the city of M\'alaga) have been defined to accurately evaluate the performance of the network under our automatic OLSR. In the experiments, our tuned OLSR configurations result in better quality of service (QoS) than the standard request for comments (RFC 3626), as well as several human experts, making it amenable for utilization in VANET configurations.

翻译：无线技术的最新进展催生了车载自组织网络（VANETs）。在此类网络中，WiFi有限的覆盖范围与节点的高移动性导致了频繁的拓扑变化和网络分割。由于这些原因，并考虑到网络中不存在中心管理实体，通过网络路由数据包成为一项具有挑战性的任务。因此，提供高效的路由策略对于VANETs的部署至关重要。本文通过定义一个优化问题，来处理优化链路状态路由（OLSR）——一种著名的移动自组织网络路由协议——的最优参数设置。为此，本文研究了一系列具有代表性的元启发式算法（粒子群优化、差分进化、遗传算法和模拟退火），以自动寻找该路由协议的最优配置。此外，我们定义了一组基于马拉加城市的真实VANET场景，以准确评估在我们的自动OLSR配置下网络的性能。实验结果表明，我们调优的OLSR配置相比标准的RFC 3626以及若干人工专家配置，能提供更好的服务质量（QoS），使其适用于VANET配置。