基于曼哈顿距离模糊C聚类及粒子群优化的中继车辆选择算法  

The relay vehicle selection algorithm based on Manhattan distance-based fuzzy C clustering and particle swarm optimization

作　　者：朱军[1] 唐万奇 李凯 ZHU Jun;TANG Wanqi;LI Kai(Institute of Electronics and Information Engineering,Anhui University,Hefei 230601,China;Institute of Creativity and Art,Shanghai University of Science and Technology,Shanghai 201210,China)

机构地区：[1]安徽大学电子信息工程学院,安徽合肥230601 [2]上海科技大学创意与艺术学院,上海201210

出　　处：《安徽大学学报（自然科学版）》2021年第4期35-40,共6页Journal of Anhui University(Natural Science Edition)

基　　金：安徽省科技重大专项(18030901010)。

摘　　要：针对车辆自组织网络中的多跳广播通信场景,基于曼哈顿距离模糊C聚类(Manhattan distance-based FCM,简称MFCM)和粒子群优化(particle swarm optimization,简称PSO),提出中继车辆选择算法.该算法先用MFCM对广播网络中的车辆进行初始分簇,再用PSO优化簇心位置.通过仿真实验对多种算法进行比较,结果表明:相对于其他算法,所提算法的信干噪比分布最优、簇维护开销最低、数据包接收率最高.Aiming at the multi-hop broadcast communication scenarios in the vehicular ad hoc network(VANET),based on Manhattan distance-based FCM(MFCM)and particle swarm optimization(PSO),a relay vehicle selection algorithm was proposed.The algorithm first used MFCM to perform initial clustering of vehicles in the broadcast network,and then used PSO to optimize the location of cluster centers.Many algorithms were compared through simulation experiments,and the results showed that compared with other algorithms,the proposed algorithm had the best signal-to-interference and noise ratio distribution,the lowest cluster maintenance overhead,and the highest packet reception rate.

关 键 词：车辆自组织网络 中继选择 模糊聚类 曼哈顿距离 粒子群优化 

分 类 号：U495[交通运输工程—交通运输规划与管理] TN929.5[交通运输工程—道路与铁道工程]