基于并行NSGA-III算法的高速列车车体侧墙结构高维多目标优化

doi:10.3901/JME.2024.06.321

机械工程学报 ›› 2024, Vol. 60 ›› Issue (6): 321-333.doi: 10.3901/JME.2024.06.321

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基于并行NSGA-III算法的高速列车车体侧墙结构高维多目标优化

柴依扬^1,2, 张乐乐^1,2, 窦伟元^1,2, 张海峰³

1. 北京交通大学机械与电子控制工程学院北京 100044;
2. 北京交通大学轨道车辆运用工程国家国际科技合作基地北京 100044;
3. 中车长春轨道客车股份有限公司长春 130062

收稿日期:2023-04-15 修回日期:2023-09-25 出版日期:2024-03-20 发布日期:2024-06-07
通讯作者: 窦伟元，男，1989年出生，博士，讲师。主要研究方向为结构优化设计与数值计算方法等。E-mail：wydou@bjtu.edu.cn
作者简介:柴依扬，男，1999年出生。主要研究方向为结构多目标优化设计。E-mail：20121397@bjtu.edu.cn
基金资助:
国家自然科学基金资助项目(52172353)。

Parallel NSGA-III Based Multi-objective Optimization for Side Wall Section Size of High-speed Train Car-body

CHAI Yiyang^1,2, ZHANG Lele^1,2, DOU Weiyuan^1,2, ZHANG Haifeng³

1. School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044;
2. National International Science and Technology Cooperation Base, Beijing Jiaotong University, Beijing 100044;
3. CRRC Changchun Railway Vehicles Co., Ltd., Changchun 130062

Received:2023-04-15 Revised:2023-09-25 Online:2024-03-20 Published:2024-06-07

摘要/Abstract

摘要： 针对传统多目标优化算法在高维优化问题中多样性不足、收敛速度慢，联合仿真优化的计算效率难以满足实际工程需求等问题，提出基于SPMD并行的NSGA-III联合仿真优化方法，由MATLAB优化器与FEM求解器构建联合仿真平台，通过调用多个求解器并行计算优化过程中的适应度函数，以提高面向复杂结构高维优化的计算效率。简单型材算例分析表明，在合理利用计算资源的情况下，提出的并行联合优化方法与传统串行方法相比，优化效率提高近一倍。以大型复杂机械结构高速列车车体侧墙为对象，建立多工况高维多目标优化问题，解析车体强度、刚度对侧墙结构不同区域型材厚度敏感性；对比分析Pareto解集，在侧墙质量减少0.96%的前提下，弯曲和扭转刚度分别增加了7.51%、5.39%，最大应力减少了12.18%。同时，相较于NSGA-II算法，NSGA-III算法可为车体结构优化设计提供更多符合期望的优化解集。

关键词: NSGA-III算法, SPMD并行, 多目标优化, 联合仿真, 高速列车车体

Abstract: For many-objective optimization problems, traditional multi-objective optimization methods are limited due to insufficient diversity, slow convergence and computational cost in co-simulation, which are all critical issues that need to be taken into account in practical engineering. This study presents a SPMD parallel-based NSGA-III co-simulation optimization method, for which the framework is constructed by MATLAB optimizer and FEM solver, respectively. To improve the efficiency for high-dimensional optimization problems, multiple solvers are employed to calculate the fitness function in parallel optimization. Analysis on a simple profile example shows that, the parallel optimization presented in this study improves approximately twice as efficiency as that of the traditional serial approach under the rational utilization of computing resources. Taking the side wall of a high-speed train car-body as an objective, high-dimensional optimization under multi-conditions is implemented to analyze the sensitivity of structural strength and stiffness to the profiles’ thickness in different areas. Considering the Pareto solution set, the total mass of the side wall is reduced by 0.96%, while the bending and torsional stiffness are increased by 7.51% and 5.39% respectively, and the maximum stress is reduced by 12.18%. Compared with NSGA-II, NSGA-III can provide more solution sets that can meet the expectations for the optimization design of vehicle car-body structure.

Key words: NSGA-III algorithm, SPMD parallel, multi-objective optimization, co-simulation, high-speed train car-body

中图分类号:

U270

柴依扬, 张乐乐, 窦伟元, 张海峰. 基于并行NSGA-III算法的高速列车车体侧墙结构高维多目标优化[J]. 机械工程学报, 2024, 60(6): 321-333.

CHAI Yiyang, ZHANG Lele, DOU Weiyuan, ZHANG Haifeng. Parallel NSGA-III Based Multi-objective Optimization for Side Wall Section Size of High-speed Train Car-body[J]. Journal of Mechanical Engineering, 2024, 60(6): 321-333.

参考文献

[1] GONG D，WANG Z，LIU G，et al. A modal frequency optimization approach for a fully-equipped car body of high-speed trains[J]. Journal of Mechanical Science and Technology，2020，34(1)：11-21.
[2] 姚曙光，田红旗，许平. 重载敞车车体结构轻量化设计[J]. 交通运输工程学报，2011，11(1)：31-35，57. YAO Shuguang，TIAN Hongqi，XU Ping. Lightweight design of carbody structure for heavy haul gondola[J]. Journal of Traffic and Transportation Engineering，2011，11(1)：31-35，57.
[3] 贺小龙，张立民，鲁连涛，等. 基于轻量化及刚度的铝合金车体承载结构参数选择研究[J]. 铁道学报，2016，38(11)：26-32. HE Xiaolong，ZHANG Limin，LU Liantao，et al. Study on selection of aluminum alloy car body bearing structural parameters based on lightweight and stiffness[J]. Journal of the China Railway Society，2016，38(11)：26-32.
[4] EBERHARD P，DIGNATH F，KÜBLER L. Parallel evolutionary optimization of multibody systems with application to railway dynamics[J]. Multibody System Dynamics，2003，9(2)：143-164.
[5] WANG D，JIANG R，WU Y. A hybrid method of modified NSGA-II and TOPSIS for lightweight design of parameterized passenger car sub-frame[J]. Journal of Mechanical Science and Technology，2016，30(11)：4909-4917.
[6] YU M，ZHANG J，ZHANG W. Multi-objective optimization design method of the high-speed train head[J]. Journal of Zhejiang University Science A，2013，14(9)：631-641.
[7] LI R，XU P，PENG Y，et al. Multi-objective optimization of a high-speed train head based on the FFD method[J]. Journal of Wind Engineering and Industrial Aerodynamics，2016，152：41-49.
[8] YUAN Y，ONG Y S，GUPTA A，et al. Objective reduction in many-objective optimization：Evolutionary multi-objective approaches and comprehensive analysis[J]. IEEE Transactions on Evolutionary Computation，2017，22(2)：189-210.
[9] DING R，DONG H，HE J，et al. A novel two-archive strategy for evolutionary many-objective optimization algorithm based on reference points[J]. Applied Soft Computing，2019，78：447-464.
[10] DI PIERRO F，KHU S T，SAVIC D A. An investigation on preference order ranking scheme for multi-objective evolutionary optimization[J]. IEEE Transactions on Evolutionary Computation，2007，11(1)：17-45.
[11] GANGULY S，SAHOO N C，DAS D. Multi-objective particle swarm optimization based on fuzzy-Pareto-dominance for possibilistic planning of electrical distribution systems incorporating distributed generation[J]. Fuzzy Sets and Systems，2013，213：47-73.
[12] DEB K，PRATAP A，AGARWAL S，et al. A fast and elitist multi-objective genetic algorithm：NSGA-II[J]. IEEE Transactions on Evolutionary Computation，2002，6(2)：182-197.
[13] ADRA S F，FLEMING P J. Diversity management in evolutionary many-objective optimization[J]. IEEE Transactions on Evolutionary Computation，2010，15(2)：183-195.
[14] DEB K，JAIN H. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach，part I：Solving problems with box constraints[J]. IEEE Transactions on Evolutionary Computation，2013，18(4)：577-601.
[15] JAIN H，DEB K. An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach，part II：Handling constraints and extending to an adaptive approach[J]. IEEE Transactions on Evolutionary Computation，2013，18(4)：602-622.
[16] 王丽萍，丰美玲，邱启仓，等. 偏好多目标进化算法研究综述[J]. 计算机学报，2019，42(6)：1289-1315. WANG Liping，FENG Meiling，QIU Qicang，et al. Survey on preference-based multi-objective evolutionary algorithms[J]. Chinese Journal of Computers，2019，42(6)：1289-1315.
[17] 张在房，徐冯，孙习武. 火箭贮箱箱底充液拉深成形工艺的多目标优化[J]. 机械工程学报，2022，58(5)：78-86. ZHANG Zaifang，XU Feng，SUN Xiwu. Multi-objective Optimization of hydroforming process of rocket tank bottom[J]. Journal of Mechanical Engineering，2022，58(5)：78-86.
[18] HU C，DAI L，YAN X，et al. Modified NSGA-III for sensor placement in water distribution system[J]. Information Sciences，2020，509：488-500.
[19] 高云凯，马超，刘哲，等. 基于NSGA-Ⅲ的白车身焊装生产平台的离散拓扑优化[J]. 上海交通大学学报，2020，54(12)：1324-1334. GAO Yunkai，MA Chao，LIU Zhe，et al. Discrete topology optimization of body-in-white welding production platform based on NSGA-III[J]. Journal of Shanghai Jiaotong University，2020，54(12)：1324-1334.
[20] 李瑞阳，王智学，禹明刚，等. 基于鲁棒能力的体系多目标组合优化[J]. 系统工程与电子技术，2019，41(5)：1034-1042. LI Ruiyang，WANG Zhixue，YU Minggang，et al. Multi-objective portfolio optimization of system- of-systems based on robust capabilities[J]. Systems Engineering and Electronics，2019，41(5)：1034-1042.
[21] IBRAHIM A，RAHNAMAYAN S，MARTIN M V，et al. 3D-RadVis Antenna：Visualization and performance measure for many-objective optimization[J]. Swarm and Evolutionary Computation，2018，39：157-176.

基于并行NSGA-III算法的高速列车车体侧墙结构高维多目标优化

Parallel NSGA-III Based Multi-objective Optimization for Side Wall Section Size of High-speed Train Car-body

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