본문 바로가기
HOME> 논문 > 논문 검색상세

논문 상세정보

Design optimization using support vector regression

Lee, Yong-Bin    (Research Engineer, The Center of Innovative Design Optimization Technology (iDOT), Hanyang University   ); Oh, Sang-Yup    (Research Engineer, The Center of Innovative Design Optimization Technology (iDOT), Hanyang University   ); Cho, Dong-Hoon    (The Center of innovative Design Optimization Technology (iDOT), Hanyang University  );
  • 초록

    Polynomial regression (PR) and kriging are standard meta-model techniques used for approximate optimization (AO). Support vector regression (SVR) is a new meta-model technique with higher accuracy and a lower standard deviation than existing techniques. In this paper, we propose a sequential approximate optimization (SAO) method using SVR. Inherited latin hypercube design (ILHD) is used as the design of experiment (DOE), and the trust region algorithm is used as the model management technique, both adopted to increase efficiency in problem solving. We demonstrate the superior accuracy and efficiency of the proposed method by solving three mathematical problems and two engineering design problems. We also compare the proposed method with other meta-models such as kriging, radial basis function (RBF), and polynomial regression.


  • 주제어

    Support vector regression .   Trust region algorithm .   Inherited latin-hypercube design .   Sequential approximate optimization.  

  • 참고문헌 (14)

    1. R. Fletcher, An Algorithm for Solving Linearly Constrained Optimization Problems, Mathematical Programming 2 (1) (1972) 133-165 
    2. J. Sacks, W. J. Welch, T. J. Mitchell and H. P. Wynn, Design and Analysis of Computer Experiments, Statistical Science 4 (4) (1989) 409-435 
    3. M. E. Johnson, L. M. Moore and D. Ylvisaker, Minimax and Maximin Distance Design, Journal of Statistical Planning and Inference 26 (2) (1990) 131-148 
    4. G. N. Vanderplaats, Dot Users Manual Version 5.0, VR&D, Colorado, USA, (1999) 12-111 
    5. M. H. McKay, R. J. Beckman and W. J. Conover, A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output form a Computer Code, Technometrics 21 (2) (1979) 239-245 
    6. G. G. Wang, Adaptive Response Surface Method Using Inherited Latin Hypercube Design Points, Transactions of the ASME 125 (2) (2003) 210-220 
    7. K. T. Fang, C. X. MA and P. Winker, Centered $L_2$-Discrepancy of Random Sampling and Latin Hypercube Design, and Construction of Uniform Designs, Mathematics of Computation 71 (237) (2000) 275-296 
    8. J. S. Park, Optimal Latin-hypercube Designs for Computer Experiments, Journal of Statistical Planning and Inference 39 (1) (1994) 95-111 
    9. M. C. Shewry, Maximum Entropy Sampling, Journal of Applied Statistics 14 (1987) 165-170 
    10. Youn-Hyun Kim, Kyung-Jin Hong and Dong-Hoon Choi, Optimal design of switched reluctance motor using two-dimensional finite element method, Journal of Applied Physics 91 (10) (2002) 6967-6969 
    11. M. J. D. Powell, Convergence Properties of a Class of Minimization Algorithms, Nonlinear Programming 2, Academic Press, New York, USA, (1975) 1-27 
    12. Alex J. Smola and Bernhard Scholkopf, A tutorial on support vector regression, Statistics and computing 14 (3) (2004) 199-222 
    13. Stella M. Clarke, an H. Griebsch, Timothy W. Simpson, Analysis of Support Vector Regression For Approximation of Complex Engineering Analyses, Journal of mechanical design 127 (6) (2005) 1077-1087 
    14. T. W. Simpson, J. Peplinkski,, P. N. Koch and J. K. Allen, Metamodels for computer based engineering design: survey and recommendations, Engineering with Computers 17 (2) (2001) 129-150 

 활용도 분석

  • 상세보기

    amChart 영역
  • 원문보기

    amChart 영역

원문보기

무료다운로드
  • 원문이 없습니다.
유료다운로드

유료 다운로드의 경우 해당 사이트의 정책에 따라 신규 회원가입, 로그인, 유료 구매 등이 필요할 수 있습니다. 해당 사이트에서 발생하는 귀하의 모든 정보활동은 NDSL의 서비스 정책과 무관합니다.

원문복사신청을 하시면, 일부 해외 인쇄학술지의 경우 외국학술지지원센터(FRIC)에서
무료 원문복사 서비스를 제공합니다.

NDSL에서는 해당 원문을 복사서비스하고 있습니다. 위의 원문복사신청 또는 장바구니 담기를 통하여 원문복사서비스 이용이 가능합니다.

이 논문과 함께 이용한 콘텐츠
이 논문과 함께 출판된 논문 + 더보기