TY - JOUR
T1 - 面向时间敏感网络的车载以太网网络架构多目标优化
AU - Zou, Yuan
AU - Sun, Wenjing
AU - Zhang, Xudong
AU - Wen, Ya
AU - Cao, Wanke
AU - Zhang, Zhaolong
N1 - Publisher Copyright:
© 2023 SAE-China. All rights reserved.
PY - 2023
Y1 - 2023
N2 - The network architecture of the vehicle electrical and electronic architecture profoundly affects communication security and certainty. For Zone-Domain based electrical and electronic architectures that use time-sensitive networks(TSN),this paper establishes for the first time the multi-objective optimization framework for network architecture with the optimization objectives of uniform number of ports,balanced load and lowest end-to-end delay of information flows. The end-to-end delay is obtained by solving the TSN traffic scheduling and the traffic scheduling is abstracted as the periodic job-scheduling problem(JSP). The multi-population genetic algorithm(MP-GA)applicable to traffic scheduling is proposed,which improves the solution effect by 16% compared with the traditional genetic algorithm. In order to solve the multi-objective optimization problems rapidly,an improved non-dominated sorting genetic algorithm(NSGA-II)is designed in this paper. The optimization efficiency is improved by 25% by introducing in the iteration factor and congestion factor to improve the algorithm with adaptive cross-variance probability. The simulation verifies the effectiveness of the multi-objective optimization framework and provides a design idea for the optimization of in-vehicle Ethernet network architecture with the introduction of TSN.
AB - The network architecture of the vehicle electrical and electronic architecture profoundly affects communication security and certainty. For Zone-Domain based electrical and electronic architectures that use time-sensitive networks(TSN),this paper establishes for the first time the multi-objective optimization framework for network architecture with the optimization objectives of uniform number of ports,balanced load and lowest end-to-end delay of information flows. The end-to-end delay is obtained by solving the TSN traffic scheduling and the traffic scheduling is abstracted as the periodic job-scheduling problem(JSP). The multi-population genetic algorithm(MP-GA)applicable to traffic scheduling is proposed,which improves the solution effect by 16% compared with the traditional genetic algorithm. In order to solve the multi-objective optimization problems rapidly,an improved non-dominated sorting genetic algorithm(NSGA-II)is designed in this paper. The optimization efficiency is improved by 25% by introducing in the iteration factor and congestion factor to improve the algorithm with adaptive cross-variance probability. The simulation verifies the effectiveness of the multi-objective optimization framework and provides a design idea for the optimization of in-vehicle Ethernet network architecture with the introduction of TSN.
KW - electrical and electronic architecture
KW - in-vehicle network architecture
KW - multi-objective optimization
KW - time-sensitive network
KW - traffic scheduling
UR - https://www.scopus.com/pages/publications/85163303305
U2 - 10.19562/j.chinasae.qcgc.2023.ep.007
DO - 10.19562/j.chinasae.qcgc.2023.ep.007
M3 - 文章
AN - SCOPUS:85163303305
SN - 1000-680X
VL - 45
SP - 746
EP - 758
JO - Qiche Gongcheng/Automotive Engineering
JF - Qiche Gongcheng/Automotive Engineering
IS - 5
ER -