标题： 基于元启发式方法的车载自组织网络通信协议自动调优 显示英文标题

标题： Automatic tuning of communication protocols for vehicular ad hoc networks using metaheuristics

摘要： 新兴的车载自组织网络（VANETs）领域涉及一组能够无需预先存在的基础设施而自发互联的通信车辆。 在这样的网络中，在最终网络部署之前对通信协议进行最佳配置至关重要。 这样，人类设计师可以事先获得网络的最佳服务质量（QoS）。 我们在本工作中考虑的问题是配置文件传输协议配置（FTC），旨在优化传输时间、丢失的数据包数量以及现实VANET场景中的传输数据量。 我们使用五种最先进的优化技术来处理FTC，并比较它们的性能。 这些算法包括：粒子群优化（PSO），差分进化（DE），遗传算法（GA），进化策略（ES）和模拟退火（SA）。 对于我们的测试，已经定义了两种典型的VANET环境实例，分别用于城市和高速公路场景。 使用ns-2（一个著名的现实VANET模拟器）进行的实验表明，PSO在两个研究的VANET实例中均优于所有比较的算法。 显示英文摘要

摘要： The emerging field of vehicular ad hoc networks (VANETs) deals with a set of communicating vehicles which are able to spontaneously interconnect without any pre-existing infrastructure. In such kind of networks, it is crucial to make an optimal configuration of the communication protocols previously to the final network deployment. This way, a human designer can obtain an optimal QoS of the network beforehand. The problem we consider in this work lies in configuring the File Transfer protocol Configuration (FTC) with the aim of optimizing the transmission time, the number of lost packets, and the amount of data transferred in realistic VANET scenarios. We face the FTC with five representative state-of-the-art optimization techniques and compare their performance. These algorithms are: Particle Swarm Optimization (PSO), Differential Evolution (DE), Genetic Algorithm (GA), Evolutionary Strategy (ES), and Simulated Annealing (SA). For our tests, two typical environment instances of VANETs for Urban and Highway scenarios have been defined. The experiments using ns- 2 (a well-known realistic VANET simulator) reveal that PSO outperforms all the compared algorithms for both studied VANET instances.