Optimization of Wheel Weight Distribution for EMU Vehicles Based on the SAGA Hybrid Algorithm

MENG Jianjun; MENG Gaoyang; LI Decang

doi:10.21656/1000-0887.410107

Volume 42 Issue 4

Apr. 2021

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Article Navigation > Applied Mathematics and Mechanics > 2021 > 42(4): 363-372

MENG Jianjun, MENG Gaoyang, LI Decang. Optimization of Wheel Weight Distribution for EMU Vehicles Based on the SAGA Hybrid Algorithm[J]. Applied Mathematics and Mechanics, 2021, 42(4): 363-372. doi: 10.21656/1000-0887.410107

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Optimization of Wheel Weight Distribution for EMU Vehicles Based on the SAGA Hybrid Algorithm

doi: 10.21656/1000-0887.410107

¹Mechatronics T& R Institute, Lanzhou Jiaotong University, Lanzhou 730070, P.R.China
²Gansu Provincial Engineering Technology Center for Informatization of Logistics & Transport Equipment, Lanzhou 730070, P.R.China
³Gansu Provincial Industry Technology Center of Logistics & Transport Equipment, Lanzhou 730070, P.R.China

Funds: The National Natural Science Foundation of China（61563027）

Received Date: 2020-04-15
Rev Recd Date: 2020-05-09
Publish Date: 2021-04-01

Abstract

Abstract

Aimed at the wheel weight deviation of EMU vehicles before leaving the factory, a mechanical model for wheel weight adjustment was established. An SA-GA hybrid algorithm proposed with the mechanism of the simulated annealing (SA) algorithm and the non-uniform mutation idea of the genetic algorithm (GA), and the mechanical model for vehicle wheel weight adjustment was numerically solved with this hybrid algorithm. The results show that, the wheel weight deviation can be reduced to less than 1.2%, which conforms to the regulations of GB/T 3317-2006. At the same time, the simulation results were compared with the numerical calculation results from the Simpack software. The comparison shows that, the calculation results based on the SA-GA hybrid algorithm are correct, and the work provides an effective calculation method for quickly optimizing the wheel weight distribution.
- EMU vehicle,
- wheel weight deviation,
- mechanical model,
- hybrid algorithm,
- software simulation

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References(16)

References

[1]	PAN D F, WANG M G, ZHU Y N, et al. An optimization algorithm for locomotive secondary spring load adjustment based on artificial immune[J]. Journal of Central South University,2013,20(12): 3497-3499.
[2]	BAO T Z, HAN K, PAN D F. Method for secondary spring load equalization of railway vehicles with two-stage spring suspension: modeling and optimal regulation[J]. Journal of Central South University,2018,25(4): 936-938.
[3]	NENOV N G, DIMITROV E N, MIHOV G S, et al. Electronic system of measuring locomotive wheels load and defining operations necessary to minimize existing differences[C]// 2006 29th International Spring Seminar on Electronics Technology. St Marienthal, Germany, 2006.
[4]	GARCIA M A P, MONTIEL O, CASTILLO O, et al. Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation[J]. Applied Soft Computing, 2009,9(3): 1102-1110.
[5]	刘明霞, 游晓明, 刘升. 基于聚度的自适应动态混沌蚁群算法[J]. 计算机工程与应用, 2019,55(3):15-22.(LIU Mingxia, YOU Xiaoming, LIU Sheng. Adaptive dynamic chaotic ant colony algorithm based on degree of aggregation[J]. Computer Engineering and Applications,2019,55(3):15-22.(in Chinese))
[6]	朱豪, 彭艺, 张申, 等. 基于改进遗传算法的自适应越区切换方案[J]. 吉林大学学报(理学版), 2020,58(1):133-139.(ZHU Hao, PENG Yi, ZHANG Shen, et al. Adaptive handover scheme based on improved genetic algorithm[J]. Journal of Jilin University(Science Edition),2020,58(1):133-139.(in Chinese))
[7]	曹孟华, 李龙, 谢红卫. 改进遗传算法在传声器阵列优化中的应用[J]. 国防科技大学学报, 2019,41(6): 126-134.(CAO Menghua, LI Long, XIE Hongwei. Application of improved genetic algorithm in microphone array optimization[J]. Journal of National University of Defense Technology,2019,41(6):126-134.(in Chinese))
[8]	宋强. 改进混合遗传算法在MTVRPTW中的建模与优化[J]. 重庆交通大学学报(自然科学版), 2018,37(9):79-86, 134.(SONG Qiang. Application of improved hybrid genetic algorithm in the modeling and optimization of multi-trip vehicle routing problem with time windows[J]. Journal of Chongqing Jiaotong University(Natural Science),2018,37(9):79-86, 134.(in Chinese))
[9]	王旭坪, 董杰, 韩涛, 等. 考虑碳排放与时空距离的冷链配送路径优化研究[J]. 系统工程学报, 2019,34(4): 555-565.(WANG Xuping, DONG Jie, HAN Tao, et al. The optimization of cold chain delivery routes considering carbonemission and temporal-spatial distance[J]. Journal of Systems Engineering,2019,34(4): 555-565.(in Chinese))
[10]	童少伟, 唐怀平. 离散系统迭代学习型瞬时最优控制及作动器位置优化研究[J]. 应用数学和力学, 2016,37(2):160-172.(TONG Shaowei, TANG Huaiping. Iterative learning instantaneous optimal control of discrete systems and optimization of actuator positions[J]. Applied Mathematics and Mechanics,2016,37(2): 160-172.(in Chinese))
[11]	LI JW, QU C W. Cloud computing task scheduling based on cultural genetic algorithm[J]. MATEC Web of Conferences,2016,40(9): 408-409.
[12]	徐磊, 翟婉明. 轨道结构随机场模型与车辆-轨道耦合随机动力分析[J]. 应用数学和力学, 2017,38(1): 67-74.(XU Lei, ZHAI Wanming. The random field model for track structures and vehicle-track coupled stochastic dynamic analysis[J]. Applied Mathematics and Mechanics,2017,38(1): 67-74.(in Chinese))
[13]	雍友, 潘迪夫, 韩锟. 车体称重调簧试验台视觉定位控制系统研究[J]. 铁道科学与工程学报, 2018,15(4): 1008-1015.(YONG You, PAN Difu, HAN Kun. Research on visual positioning control system of vehicle weighing spring test bench[J]. Journal of Railway Science and Engineering,2018,15(4): 1008-1015.(in Chinese))
[14]	李元香, 蒋文超, 项正龙, 等. 基于弛豫模型的模拟退火算法温度设置方法[J/OL]. 计算机学报, 2019,42: 121[2020-05-09］. http://kns.cnki.net/kcms/detail/11.1826.TP.20191212.1201.002.html.(LI Yuanxiang, JIANG Wenchao, XIANG Zhenglong, et al. Relaxation model based temperature setting methods for simulated annealing algorithm[J/OL]. Chinese Journal of Computers,2019,42: 121[2020-05-09］. http://kns.cnki.net/kcms/detail/11.1826.TP.20191212.1201.002.html.(in Chinese))
[15]	王彦超, 沈云发, 黄捍东. 基于快速模拟退火算法的叠前三参数同步反演[J]. 工程地球物理学报, 2012,9(2): 221-226.(WANG Yanchao, SHEN Yunfa, HUANG Handong. Pre-stack simultaneous three-parameter inversion based on fast simulated annealing algorithm[J]. Chinese Journal of Engineering Geophysics,2012,9(2): 221-226.(in Chinese))
[16]	谢裕江, 刘高. 小波边缘分析与建模的波阻抗反演算法的改进: 以中国MOU气田盒8段储层分布预测为例[J]. 浙江大学学报(工学版), 2013,47(9): 1680-1684, 1696.(XIE Yujiang, LIU Gao. Algorithmic modification of acoustic impedance inversion based on wavelet edge analysis and modelling: a case of reservoir distribution prediction in h8 segment of MOU gas field, China[J]. Journal of Zhejiang University(Engineering Science),2013,47(9): 1680-1684, 1696.(in Chinese))