中国机械工程 ›› 2011, Vol. 22 ›› Issue (4): 488-493.

• 车辆工程 • 上一篇    下一篇

基于自适应径向基函数的整车耐撞性多目标优化

陈国栋;韩旭;刘桂萍;赵子衡
  

  1. 湖南大学汽车车身先进设计制造国家重点实验室,长沙,410082
  • 出版日期:2011-02-25 发布日期:2011-03-15
  • 基金资助:
    国家自然科学基金资助项目(10725208);国家重点基础研究发展计划(973计划)资助项目(2010CB832700);高等学校博士学科点专项科研基金资助项目(200805321034);国家科技重大专项项目(2010ZX04017-013-005) 
    National Natural Science Foundation of China(No. 10725208);
    National Program on Key Basic Research Project (973 Program)(No. 2010CB832700);
    Specialized Research Fund for the Doctoral Program of Higher Education of China(No. 200805321034);
    National Science and Technology Major Project ( No. 2010ZX04017-013-005)

Multi-objective Design Optimization on Crashworthiness of Full Vehicle Based on Adaptive Radial Basis Function

Chen Guodong;Han Xu;Liu Guiping;Zhao Ziheng
  

  1. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body,Hunan University,Changsha,410082 
  • Online:2011-02-25 Published:2011-03-15
  • Supported by:
     
    National Natural Science Foundation of China(No. 10725208);
    National Program on Key Basic Research Project (973 Program)(No. 2010CB832700);
    Specialized Research Fund for the Doctoral Program of Higher Education of China(No. 200805321034);
    National Science and Technology Major Project ( No. 2010ZX04017-013-005)

摘要:

提出自适应径向基函数代理模型,并结合微型多目标遗传算法对整车耐撞性进行优化。在每个迭代步中,以最优拉丁方进行样本点设计,以遗传最优拉丁方进行测试点设计,通过隔代映射遗传算法对径向基函数代理模型的误差进行评价并获得最优光滑参数。将测试点不断添加到样本空间,直至耐撞性各个目标代理模型在测试点的误差都达到要求,再采用贪婪算法将最后迭代步的测试点筛选到样本空间以进一步提高精度。最后采用微型多目标遗传算法对达到许可误差的各个自适应径向基函数模型进行优化,获得Pareto前沿面,根据工程要求或工程人员的经验权衡耐撞性,优化各个目标之间的关系以获得不同最优妥协解。

关键词:

Abstract:

An ARBF model method was suggested and combined with
micro multi-objective genetic algorithm(μMOGA) to solve vehicle crashworthiness. In each iterative,sampling points were obtained by the optimal Latin hypercube design, while testing points were obtained by the inherit optimal Latin hypercube design, this method
regarded the errors of testing points as fitness of intergeneration projection genetic algorithm (IP-GA), assessed the model systematically and got the optimal smooth parameters to maximize model accuracy, testing points added to sample space until reaching errors
 allowable of each crashworthiness objective. Then greed algorithm was adopted to filter the testing points from the last iterative to sampling space to increase accuracy. At last, μMOGA was applied to optimize the ARBF,and got Pareto and balanced each objective to get different best compromise solutions according to engineer experiments or
engineerring requirements.

Key words: adaptive radial basis function(ARBF), smooth parameter, multi-objective optimization, vehicle crashworthiness

中图分类号: