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http://dx.doi.org/10.3795/KSME-A.2005.29.9.1199

Sequential Approximate Optimization Using Kriging Metamodels  

Shin Yongshik (한양대학교 대학원 기계설계학과)
Lee Yongbin (한양대학교 대학원 기계설계학과)
Ryu Je-Seon (한양대학교 최적설계신기술연구센터)
Choi Dong-Hoon (한양대학교 최적설계신기술연구센터)
Publication Information
Transactions of the Korean Society of Mechanical Engineers A / v.29, no.9, 2005 , pp. 1199-1208 More about this Journal
Abstract
Nowadays, it is performed actively to optimize by using an approximate model. This is called the approximate optimization. In addition, the sequential approximate optimization (SAO) is the repetitive method to find an optimum by considering the convergence of an approximate optimum. In some recent studies, it is proposed to increase the fidelity of approximate models by applying the sequential sampling. However, because the accuracy and efficiency of an approximate model is directly connected with the design area and the termination criteria are not clear, sequential sampling method has the disadvantages that could support an unreasonable approximate optimum. In this study, the SAO is executed by using trust region, Kriging model and Optimal Latin Hypercube design (OLHD). Trust region is used to guarantee the convergence and Kriging model and OLHD are suitable for computer experiment. finally, this SAO method is applied to various optimization problems of highly nonlinear mathematical functions. As a result, each approximate optimum is acquired and the accuracy and efficiency of this method is verified by comparing with the result by established method.
Keywords
Kriging Model; Optimal Latin Hypercube Design; Sequential Approximate Optimization; Trust Region Algorithm;
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