摘要
对VANET路由的自适应能力问题进行研究,提出一种VANET路由性能推理系统。该系统在粒子群算法和遗传算法的基础上,使用遗传粒子群算法的自适应模糊推理系统(Genetic Algorithm Particle Swarm Optimization Adaptive Neuro-Fuzzy Inference System,GAPSO-ANFIS)完善了VANET最优路由机制推理系统。通过效用函数对网络最优路由机制进行推理,并在按需距离矢量路由协议(Ad hoc On-Demand Distance Vector Routing,AODV)、目的序列距离矢量路由协议(Destination Sequenced Distance-Vector,DSDV)及动态源路由协议(Dynamic Source Routing,DSR)等3种典型VANET路由协议仿真数据集下进行了最优路由协议推理验证。验证结果表明,改进后的VANET路由性能推理系统具有更高的准确性,能够提升ANFIS进行参数寻优和跳出局部最优解的能力,可以解决当前VANET自适应路由技术中对最优路由协议选择不够准确的问题。
The adaptive capability of VANET routing is studied and a performance reasoning system of VANET routing is proposed.On the basis of particle swarm optimization and genetic algorithm,the system uses the genetic algorithm particle swarm optimization adaptive neuro-fuzzy inference system(GAPSO-ANFIS)to improve the VANET optimal routing mechanism reasoning scheme,through the utility function infers the optimal routing mechanism of the network,and the optimal routing protocol reasoning is verified by the simulation data sets of three typical VANET routing protocols,such as ad-hoc on-demand distance vector routing(AODV),destination sequenced distance-vector(DSDV)and dynamic source routing(DSR).The verification results show that the improved VANET routing performance reasoning system has higher accuracy,can improve the ability of ANFIS to search parameters and jump out of the local optimal solution,and can solve the problem that the optimal routing protocol is not accurate enough in the current VANET adaptive routing technology.
作者
师亚莉
黄楠
杨军华
杨志
SHI Yali;HUANG Nan;YANG Junhua;YANG Zhi(School of Electronic Engineering,Xi’an University of Posts and Telecommunications,Xi’an 710121,China)
出处
《西安邮电大学学报》
2022年第5期27-34,共8页
Journal of Xi’an University of Posts and Telecommunications
基金
陕西省重点研发计划项目(2022GY-055)。
关键词
车辆自组织网络
路由性能推理
自适应模糊推理系统
遗传算法
粒子群算法
vehicular Ad-hoc network
routing performance reasoning
adaptive neuro-fuzzy inference system
genetic algorithm
particle swarm optimization
