首页|Multivariate adaptive regression splines based simulation optimization using move-limit strategy

Multivariate adaptive regression splines based simulation optimization using move-limit strategy

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This paper makes an approach to the approximate optimum in structural design,which combines the global response surface (GRS) based multivariate adaptive regression splines (MARS) with Move-Limit strategy (MLS).MARS is an adaptive regression process,which fits in with the multidimensional problems.It adopts a modified recursive partitioning strategy to simplify high-dimensional problems into smaller highly accurate models.MLS for moving and resizing the search sub-regions is employed in the space of design variables.The quality of the approximation functions and the convergence history of the optimization process are reflected in MLS.The disadvantages of the conventional response surface method (RSM) have been avoided,specifically,highly nonlinear high-dimensional problems.The GRS/MARS with MLS is applied to a high-dimensional test function and an engineering problem to demonstrate its feasibility and convergence,and compared with quadratic response surface (QRS) models in terms of computational efficiency and accuracy.

global response surface (GRS)multivariate adaptive regression splines (MARS)Move-Limit strategy (MLS)quadratic response surface (QRS)

MAO Hu-ping、WU Yi-zhong、CHEN Li-ping

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National CAD Supported Software Engineering Centre, Huazhong University of Science and Technology,Wuhan 430074, P. R. China

Mechatronic Engineering Institute, North University of China, Taiyuan 030051, P. R. China

Project supported by the National Natural Science Foundation of Chinaand the National Hightechnology Research and Development Program of China

507750842006AA04Z121

2011

10.1007/sl1741-011-0783-2

上海大学学报(英文版)
上海大学

上海大学学报(英文版)

影响因子：0.196
ISSN：1007-6417
年,卷(期)：2011.15(6)
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