Vehicular ad-hoc network (VANET) is a key enabling technology of intelligent transportation systems. VANETs are characterised by the rapidly changing topology and the unbounded network size. These characteristics present a range of challenges to different VANET applications such as routing and security. Clustering has strongly presented itself as an efficient solution to such challenges. In this study, the authors formulate the clustering algorithm as a many-objective optimisation problem. Then, they propose a unified framework to optimise the configuration parameters arbitrary clustering algorithms. Three many-objective metaheuristic optimisation techniques, ESPEA, MOEA/DD and NSGA-III, are compared in context of this framework, and various commonly used quality indicators are utilised to identify the metaheuristic with the best quality of solutions. The proposed framework is then used to optimise a recent clustering algorithm. Using the optimal configuration resulting from the proposed framework significantly improves the performance of the clustering algorithm under-test compared to the non-optimised algorithm as well as other clustering approaches. This is demonstrated by the simulation results which showed up to 182% improvement in the cluster head lifetime and a reduction of 36% in the clustering packets overhead in the highway environment.

中文翻译：

---

生物启发式元启发式框架，用于VANET中的集群优化

车载自组织网络（VANET）是智能交通系统的关键启用技术。VANET的特点是拓扑结构快速变化，网络规模无限制。这些特性给不同的VANET应用带来了一系列挑战，例如路由和安全性。集群已将自身作为解决此类挑战的有效解决方案。在这项研究中，作者将聚类算法公式化为一个多目标优化问题。然后，他们提出了一个统一的框架来优化配置参数的任意聚类算法。在此框架下，比较了三种多目标元启发式优化技术ESPEA，MOEA / DD和NSGA-III，并且使用各种常用的质量指标来识别具有最佳解决方案质量的元启发式方法。然后，将所提出的框架用于优化最近的聚类算法。与未优化的算法以及其他聚类方法相比，使用由所提出的框架得出的最佳配置可以显着提高被测聚类算法的性能。仿真结果证明了这一点，在高速公路环境中，簇头寿命提高了182％，簇数据包开销减少了36％。与未优化的算法以及其他聚类方法相比，使用由所提出的框架得出的最佳配置可以显着提高被测聚类算法的性能。仿真结果证明了这一点，在高速公路环境中，簇头寿命提高了182％，簇数据包开销减少了36％。与未优化的算法以及其他聚类方法相比，使用由所提出的框架得出的最佳配置可以显着提高被测聚类算法的性能。仿真结果证明了这一点，在高速公路环境中，簇头寿命提高了182％，簇数据包开销减少了36％。