Sequential kriging optimization using multiple-fidelity evaluations
- 971 Downloads
When cost per evaluation on a system of interest is high, surrogate systems can provide cheaper but lower-fidelity information. In the proposed extension of the sequential kriging optimization method, surrogate systems are exploited to reduce the total evaluation cost. The method utilizes data on all systems to build a kriging metamodel that provides a global prediction of the objective function and a measure of prediction uncertainty. The location and fidelity level of the next evaluation are selected by maximizing an augmented expected improvement function, which is connected with the evaluation costs. The proposed method was applied to test functions from the literature and a metal-forming process design problem via finite element simulations. The method manifests sensible search patterns, robust performance, and appreciable reduction in total evaluation cost as compared to the original method.
KeywordsMultiple fidelity Surrogate systems Kriging Efficient global optimization Computer experiments
Unable to display preview. Download preview PDF.
- Ackley DH (1987) A connectionist machine for genetic hill-climbing. Kluwer, BostonGoogle Scholar
- Audet C, Dennis JE Jr, Moore DW, Booker A, Frank PD (2000) A surrogate-model-based method for constrained optimization. In: Proceedings of the 8th AIAA/NASA/USAF/ISSMO symposium on multidisciplinary analysis and optimization, AIAA-2000-4891, Long Beach, 6–8 September 2000Google Scholar
- Cressie NAC (1993) Statistics for spatial data, revised edn. Wiley, New YorkGoogle Scholar
- Edy D, Averill RC, Punch WF III, Goodman ED (1998) Evaluation of injection island GA performance on flywheel design optimization. In: Parmee IC (ed) Adaptive computing in design and manufacture. Springer, Berlin Heidelberg New YorkGoogle Scholar
- Hutchinson MG, Unger ER, Mason WH, Grossman B, Haftka RT (1994) Variable-complexity aerodynamic optimization of a high speed civil transport wing. J Aircr 31:110–116Google Scholar
- Kaufman M, Balabanov V, Burgee SL, Giunta AA, Grossman B, Mason WH, Watson LT (1996) Variable-complexity response surface approximations for wing structural weight in HSCT design. Proceedings of the 34th aerospace sciences meeting and exhibit, AIAA-96-0089, Reno, 15–18 January 1996Google Scholar
- Koehler JR, Owen AB (1996) Computer experiments. In: Ghosh S, Rao CR (eds) Handbook of statistics, vol 13. Elsevier, AmsterdamGoogle Scholar
- Kushner HJ (1964) A new method of locating the maximum point of an arbitrary multipeak curve in the presence of noise. J Basic Eng 86:97–106Google Scholar
- O'Hagan A (1989) Comment: design and analysis of computer experiments. Stat Sci 4:430–432Google Scholar
- Sasena MJ (2002) Flexibility and efficiency enhancements for constrained global design optimization with kriging approximations. Ph.D. dissertation, University of Michigan, Ann ArborGoogle Scholar
- Schonlau M (1997) Computer experiments and global optimization. Ph.D. dissertation, University of Waterloo, WaterlooGoogle Scholar