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A Heuristic Algorithm Based on Ant Colony Optimization for Multi-objective Routing in Vehicle Ad Hoc Networks

机译:车辆自组织网络中基于蚁群优化的多目标路由启发式算法

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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.
机译:车辆自组织网络(VANET)为交通领域的创新提供了机会,从而为智能交通系统(ITS)提供服务。由于VANET的特性(例如高度动态的网络拓扑结构和频繁的不连续性),因此希望在给定的时刻建立用于消息的快速传递的路由,而断开的可能性很小。这导致了多目标问题。在这项工作中,我们提出了一种基于ACO(蚁群优化)的多目标启发式算法,以考虑最短路径(一条路径中的节点数)和最低断开概率之间的最佳承诺来找到路径。在三种不同情况下进行了仿真:静态路由,带障碍的静态路由和动态路由。结果非常有希望,只需少量的计算即可获得,并允许使用该算法进行实时优化。

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