Kriging metamodel management in the design optimization of a CNG injection system

Mathematics and Computers in Simulation

Volumn 79, Issue 8, 2009, Pages 2345-2360

  Dellino, G ^a   Lino, P ^b   Meloni, C ^b   Rizzo, A ^b  

^a UNIVERSITY OF SIENA   (Italy)

^b POLITECNICO DI BARI   (Italy)

Author keywords

Design of Experiments; Evolutionary algorithms; Kriging metamodels; Metamodel management; Optimization

Indexed keywords

DESIGN OF EXPERIMENTS; ENERGY RESOURCES; EVOLUTIONARY ALGORITHMS; GAS INDUSTRY;

COMPARATIVE ANALYSIS; COMPRESSED NATURAL GAS ENGINES; COMPUTATIONAL RESULTS; DESIGN OPTIMIZATIONS; ENGINEERING DESIGN OPTIMIZATIONS; INJECTION SYSTEMS; KRIGING; KRIGING METAMODELS; MANAGEMENT ISSUES; METAMODEL MANAGEMENT; MULTI OBJECTIVES; PERFORMANCE ASSESSMENTS;

OPTIMIZATION;

EID: 63449133485     PISSN: 03784754     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.matcom.2009.01.013     Document Type: Article

References (25)

1. Büche D., Schraudolph N.N., and Koumoutsakos P. Accelerating evolutionary algorithms with Gaussian process fitness function models. IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews 35 2 (2005) 183-194
2. Chen V.C.P., Tsui K.-L., Barton R.R., and Meckesheimer M. A review on design, modeling and applications of computer experiments. IIE Transactions 38 (2006) 273-291
3. Deb K., Pratap A., Agarwal S., and Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6 2 (2002) 182-197
4. Dellino G., Lino P., Meloni C., and Rizzo A. Multidisciplinary design optimization of a pressure controller for CNG injection systems. Proceedings of the 2006 IEEE Conference on Computer-Aided Control Systems Design. Munich, Germany (2006) 2689-2694
5. Guzzella L., and Onder C.H. Introduction to Modeling and Control of Internal Combustion Engine Systems (2004), Springer-Verlag, Berlin-Heidelberg
6. den Hertog D., Kleijnen J.P.C., and Siem A.Y.D. The correct Kriging variance estimated by bootstrapping. Journal of the Operational Research Society 57 4 (2006) 400-409
7. Huang D., Allen T.T., Notz W.I., and Zeng N. Global optimization of stochastic black-box systems via sequential Kriging meta-models. Journal of Global Optimization 34 (2006) 441-466
8. Jin Y. A comprehensive survey of fitness approximation in evolutionary computation. Soft Computing 9 1 (2005) 3-12
9. Jin R., Chen W., and Simpson T.W. Comparative studies of metamodelling techniques under multiple modelling criteria. Structural Multidisciplinary Optimization 23 (2001) 1-13
10. Jones D.R. A taxonomy of global optimization methods based on response surfaces. Journal of Global Optimization 2 (2001) 345-383
11. Kodiyalam S., and Sobieszczanski-Sobieski J. Multidisciplinary Design Optimization - Some formal methods, framework requirements, and application to vehicle design. International Journal of Vehicle Design 25 1/2 (2001) 3-22
12. Lino P., Maione B., Amorese C., and De Matthaeis S. Modeling and predictive control of a new injection system for compressed natural gas engines. Proceedings of the 2006 IEEE Conference on Control Applications. Munich, Germany (2006)
13. S.N. Lophaven, H.B. Nielsen, J. Søndergaard, DACE - A Matlab Kriging Toolbox (Version 2.0), Technical Report IMM-REP-2002-12, Informatics and Mathematical Modelling, DTU, 2002, pp. 1-34.
14. S.N. Lophaven, H.B. Nielsen, J. Søndergaard, Aspects of the Matlab Toolbox DACE, Technical Report IMM-REP-2002-13, Informatics and Mathematical Modelling, DTU, 2002, pp. 1-44.
15. Martin J.D., and Simpson T.W. Use of Kriging models to approximate deterministic computer models. AIAA Journal 43 4 (2005) 853-863
16. Mera N.S. Efficient optimization processes using Kriging approximation models in electrical impedance tomography. International Journal for Numerical Methods in Engineering 69 (2007) 202-220
17. Montgomery D.C. Design and Analysis of Experiments (2005), John Wiley & Sons
18. Ong Y.S., Nair P.B., and Keane A.J. Evolutionary optimization of computationally expensive problems via surrogate modeling. AIAA Journal 41 4 (2003) 687-696
19. Papalambros P.Y., and Wilde D.J. Principles of Optimal Design (2000), Cambridge University Press
20. Ratle A. Accelerating the convergence of evolutionary algorithms by fitness landscape approximation. Lecture Notes of Computer Science 1498 (1998) 87-96
21. Santner T.J., Williams B.J., and Notz W.I. The Design and Analysis of Computer Experiments (2003), Springer
22. Sasena M.J., Papalambros P.Y., and Goovaerts P. Exploration of metamodelling sampling criteria for constrained global optimization. Engineering Optimization 34 (2002) 263-278
23. Thanassoulis E. Introduction to the Theory and Application of Data Envelopment Analysis (2001), Kluwer Academic Publisher
24. V. Torczon, M.V. Trosset, Using approximations to accelerate engineering design optimization, NASA Langley Research Center, Hampton, ICASE Report 98-33, 1998, pp. 1-15.
25. van Beers W.C.M., and Kleijnen J.P.C. Kriging interpolation in simulation: a survey. In: Ingalls R.G., Rossetti M.D., Smith J.S., and Peters B.A. (Eds). Proceedings of the 2004 Winter Simulation Conference (2004) 113-121