Abstract
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.
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Project supported by the National Natural Science Foundation of China (Grant No.50775084), and the National Hightechnology Research and Development Program of China (Grant No.2006AA04Z121)
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Mao, Hp., Wu, Yz. & Chen, Lp. Multivariate adaptive regression splines based simulation optimization using move-limit strategy. J. Shanghai Univ.(Engl. Ed.) 15, 542–547 (2011). https://doi.org/10.1007/s11741-011-0783-2
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DOI: https://doi.org/10.1007/s11741-011-0783-2