基于蚁群优化的车辆自组织网络多目标路由启发式算法

Rodrigo Silva, H. S. Lopes, W. Godoy
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引用次数: 25

摘要

车辆自组织网络(VANET)为交通领域的创新提供了机会,使智能交通系统(ITS)的服务成为可能。由于VANET的特点,例如高度动态的网络拓扑结构和频繁的不连续,因此希望在给定时刻建立快速传递消息的路由,并且具有低断开概率。这导致了一个多目标问题。在这项工作中,我们提出了基于蚁群优化的多目标启发式算法,以考虑最短路径(路径中的节点数)和最低断开概率之间的最佳承诺来寻找路径。通过三种不同的场景进行了仿真:静态路由、带障碍物的静态路由和动态路由。结果非常有希望,计算量很小,并且允许使用该算法进行实时优化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Heuristic Algorithm Based on Ant Colony Optimization for Multi-objective Routing in Vehicle Ad Hoc Networks
Vehicle Ad hoc Network (VANET) provides an opportunity for innovation in the transportation area, enabling services for Intelligent Transportation System (ITS). Because of VANET features, such as highly dynamic networks topology and frequent discontinuity, it is desirable to establish, at a given moment, routes for fast delivery of messages, having a low probability of disconnection. This leads to a multiobjective problem. In this work we propose multiobjective heuristic algorithm, based on ACO (Ant Colony Optimization) to find routes considering the best commitment between the shortest path (number of nodes in a route) and the lowest probability of disconnection. Simulations were done with three different scenarios: static routing, static routing with obstacles, and dynamic routing. Results were very promising, obtained with small computational effort, and allowing the use of the algorithm for real-time optimization.
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