用并行多目标算法自动优化车联网路由参数，提升通信性能。
Parallel multi-objective metaheuristics for smart communications in vehicular networks
- 采用进化算法与群智能方法并行搜索最优路由配置
- 优化后配置性能优于现有先进方案，计算效率超87%
- 适合需要高效车联网优化的系统设计人员
本文分析了两种并行多目标软计算算法在车载自组网中自动搜索高质Ad hoc On Demand Vector（AODV）路由协议参数的应用。这些方法基于进化算法和群体智能方法。实验结果表明，本研究所提出的优化算法生成的配置性能优于其他现有最优方案。所有并行版本均实现了超过87%的计算效率提升。因此，本文提出的方法为改善车载通信提供了一个高效框架。
原文摘要 · Abstract (English)
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.
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