Optimization design of multi-objective particle swarm in crashworthiness based on sequential response surface method

被引:6
作者
Sun, Guangyong [1 ]
Li, Guangyao [1 ]
Zhong, Zhihua [1 ]
Zhang, Yong [1 ]
机构
[1] State Key Laboratory of Advanced Design and Manufacture for Vehicle Body, Hunan University
来源
Jixie Gongcheng Xuebao/Journal of Mechanical Engineering | 2009年 / 45卷 / 02期
关键词
Crashworthiness simulation; Multi-objective; Optimization design; Particle swarm optimization;
D O I
10.3901/JME.2009.02.224
中图分类号
学科分类号
摘要
Automotive structural optimization related to crashworthiness and energy absorption capability is a process of optimization which involves multi-variable, multi-constraint and multi-objective. To solve the problem of low precision of conventional response surface method caused by the approaching in the whole design space and the problem of traditional single-objective optimization design as it can only optimize one objective, a successive approximation method is proposed. Through moving and zooming, the region of interest is constantly renewed in the design space. In the different regions of interest, the experimental design, a high-precision approximation model that can represent the actual collision process, and multi-objective particle swarm optimization algorithm are combined, then a set of minimized non-inferior solutions for objective functions are obtained. By using the minimum distance solution selection method, a set of solutions with best crashworthiness effect are rapidly and effectively selected from the set of non-inferior solutions, and taken as the center of region of interest of next iterative step until converging to the best solution, then all the objective functions of the final solution are improved. Numerical example indicates that this method has high precision and strong engineering practicability.
引用
收藏
页码:224 / 230
页数:6
相关论文
共 12 条
[1]  
Zhang W., Zhong Z., Computer simulation of the crashworthiness of the energy absorbing front end of a passenger car, Automotive Engineering, 24, 1, pp. 6-9, (2002)
[2]  
Koch P.N., Yang R.J., Gu L., Design for six sigma through robust optimization, Struct. Multidist. Optim., 26, pp. 235-248, (2004)
[3]  
Kurtaran H., Eskandarian A., Marzouguiel D., Crash-worthiness design optimization using uccessive response surface approximation, Computational Mechanics, 29, pp. 409-421, (2002)
[4]  
Zhang J., Ke Y., A research on the optimization of auto panel forming process with sequential response surface method, Automotive Engineering, 27, 2, pp. 246-250, (2005)
[5]  
Stander N., Craig K.J., On the robustness of the successive response surface method for simulation-based optimization, Engineering Computations, 19, pp. 431-450, (2002)
[6]  
Xiong J., Qiao Z., Han Z., Optimum aerodynamic design of transonic wing based on response surface methodology, Acta Aeronautica Et Astronautica Sinica, 27, 3, pp. 399-402, (2006)
[7]  
Kennedy J., Eberhart R.C., Particle swarm optimization, Proc. of IEEE International Conference on Neural Networks, (1995)
[8]  
Eberhart R.C., Shi Y., Particle swarm optimization: Develoments, applications and resources, Proc. of Congress on Evolutionary Computation 2001, pp. 81-86, (2001)
[9]  
Parsopoulos K.E., Varhatis M.N., Particle swarm optimization method in multiobjective problems, Proc. of ACM Symp. on Applied Computing, pp. 603-607, (2002)
[10]  
Hu X., Eberhart R.C., Multiobjective using dynamic neighborhood particle swarm optimization, Proc. of Congress Evolutionary Computation, Honolulu, Hawaii, USA, pp. 1677-1681, (2002)