A Kriging-based probabilistic optimization method with an adaptive search region

Engineering Optimization

Volumn 38, Issue 5, 2006, Pages 541-555

  Jeong, Shinkyu ^a   Obayashi, Shigeru ^a   Yamamoto, Kazuomi ^b  

^a TOHOKU UNIVERSITY   (Japan)

^b JAPAN AEROSPACE EXPLORATION AGENCY   (Japan)

Author keywords

Adaptive search region; Functional analysis of variance; Kriging model

Indexed keywords

AIRFOILS; CONSTRAINT THEORY; MATHEMATICAL MODELS; OPTIMIZATION; PROBABILITY;

ADAPTIVE SEARCH REGION; FUNCTIONAL ANALYSIS OF VARIANCE; KRIGING MODEL;

ONLINE SEARCHING;

EID: 33745636151     PISSN: 0305215X     EISSN: 10290273     Source Type: Journal    
DOI: 10.1080/03052150600627073     Document Type: Article

References (15)

1. Jeong, S., Murayama, M., and Yamamoto, K., Efficient optimization design method using Kriging model. J. Aircraft, 2005, 42, 413-420.
2. Jone, D.R., Schonlau, M., and Welch, W.J, Efficient global optimization of expensive black-box function. J. Global Optimiz., 1998, 13, 455-492.
3. Keane, A.J., Wing optimization using design of experiment, response surface, and data fusing methods. J. Aircraft, 2003, 40(5), 741-750.
4. Kim, H., Sasaki, D., Obayashi, S., and Nakahashi, K., Aerodynamic optimization of supersonic transport wing using unstructured adjoint method. AIAA J., 2001, 39(6), 1011-1020.
5. McKay, M.D., Beckman, R.J. and Conover, W.J., A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 1979, 21(2), 239-245.
6. Mardia, K.V. and Marshall, R.J., Maximum likelihood estimation of models for residual covariance in spatial regression. Biometrika, 1984, 71, 135-146.
7. Matthias, S., Computer experiments and global optimization. PhD dissertation, University of Waterloo, Ontario, 1997.
8. Myers, R.H. and Montgomery, D.C, Response Surface Methodology: Process and Product Optimization Using Designed Experiments, 1995 (John Wiley: New York).
9. Obayashi, S. and Guruswamy, G.P., Convergence acceleration of a aeroelastic Navier-Stokes solver. AIAA J., 1995, 33(6), 1134-1141.
10. Oyama, A, Obayashi, S., and Nakahashi, K., Euler/Navier-Stokes optimization of supersonic wing design based on evolutionary algorithms. AIAA J., 1999, 37(10), 1327-1328.
11. Owen, A.B., Orthogonal arrays for computer experiments, integration and visualization. Stat. Sin., 1992, 2, 439-452.
12. Reuther, J. and Jameson, A., Aerodynamic shape optimization of wing and wing-body configuration using control theory, AIAA, Paper 95-0123, 1995.
13. Sack, J., Welch, W.J., Mitchell, T.J., and Wynn, H.P., Design and analysis of computer experiments (with discussion). Stat. Sci., 1989, 4, 409-435.
14. Sobieczky, H., Parametric airfoils and wings. In Recent Development of Aerodynamic Design Methodologies: Inverse Design and Optimization, edited by K. Fuji and G.S. Dulikaravich, pp. 71-87, 1999 (Vieweg: Braunschweig).
15. Takami, H., Kita, H., and Kobayashi, S., Multi-objective optimization by genetic algorithms: a review. In Proceedings of 1996 IEEE International Conference on Evolutionary Computation, 1996, pp. 517-522.