中文摘要：

为在包含方面影响，僵硬，自然频率，和结构重量的多重标准下面优化汽车的门的一个方法被介绍。Metamodelingtechnique 被采用构造近似代替高计算的模拟模型。为僵硬和自然频率的接近的功能用 Taylorseries 近似被构造。三种流行近似技术，即多项式反应表面(PRS ) ，逐步的回归(SR ) ，和 Kriging 在方面影响功能的构造在他们的精确性上被学习。一致设计被采用取样门影响分析的设计空间。优化问题被一个多客观的基因算法解决。SR 技术以在这研究的精确性比 PRS 和 Kriging 技术优异，这被发现。Thenumerical 结果证明方法成功地产生散布得好的 Pareto 最佳的集合。从这个 Pareto 最佳的集合，决定制造者能根据车辆程序和它的应用程序选择最合适的设计。

英文摘要：

A method for optimizing automotive doors under multiple criteria involving the side impact, stiffness, natural frequency, and structure weight is presented. Metamodeling technique is employed to construct approximations to replace the high computational simulation models. The approximating functions for stiffness and natural frequency are constructed using Taylor series approximation. Three popular approximation techniques, i. e. polynomial response surface （PRS）, stepwise regression （SR）, and Kriging are studied on their accuracy in the construction of side impact functions. Uniform design is employed to sample the design space of the door impact analysis. The optimization problem is solved by a multi-objective genetic algorithm. It is found that SR technique is superior to PRS and Kriging techniques in terms of accuracy in this study. The numerical results demonstrate that the method successfully generates a well-spread Pareto optimal set. From this Pareto optimal set, decision makers can select the most suitable design according to the vehicle program and its application.