Multi-vehicle Ad-hoc Network Routing Algorithm with Ant and Quantum Particle Swarm Optimization Combined

Abstract

In order to improve data transfer efficiency of the vehicle self-organizing networks and reduce end transmission delay, this paper presents a Multi-vehicle ad-hoc network routing algorithm with Ant and quantum particle swarm optimization combined (AQVT) . The algorithm based on the thinking of ant colony algorithm that can find the optimal path, through distance relationship between vehicle and roadside access point and vehicle location information to as measures of ant search path, to find the optimal vehicles network routing. and uses the idea of QPSO algorithm that can global search to enhance the global convergence of the algorithm. Experimental results show that, compared MROFDM algorithm and DRIP algorithm, AQVT algorithm can achieve higher data delivery ratio and obtain a lower-end latency. Keywords—Vehicle self-organizing network; more routing For the study of VANET, researchers achieved a certain stage of the research results. A movement range oriented forwarding and dynamic multi-copies routing protocol (MROFDM) is proposed in literature(6), with the similarity among the movement models and the local real-time information of vehicles, this protocol sended packets to the movement scope of destination node. At the same time, the copy equilibrium strategy was used to dynamically adjust copy number of different types of packets. MROFDM with some existing multi-copies routing protocols were compare in simulation. A Distributed Real-time Information based Routing Protocol in vehicular ad-hoc networks (DRIP) is proposed in literature(7), Based on the proposed Distributed Real-time Delay Evaluation Scheme (DRES), vehicles obtain the real-time information for the network status of each road, and according to the delay evaluation of each road(8), vehicles use DRIP for an effective data delivery.