Managing approximation models in multiobjective optimization

Structural and Multidisciplinary Optimization

Volumn 24, Issue 2, 2002, Pages 141-156

  Yang, B S ^a   Yeun, Y S ^b   Ruy, W S ^a  

^a Seoul National University   (South Korea)

^b DEAKIN UNIVERSITY   (Australia)

Author keywords

Approximation model; Model management framework; Multiobjective; Optimization

Indexed keywords

APPROXIMATION THEORY; COMPUTATIONAL METHODS; COMPUTER SIMULATION; CONVERGENCE OF NUMERICAL METHODS; GENETIC ALGORITHMS; ITERATIVE METHODS; MATHEMATICAL MODELS;

MODEL MANAGEMENT FRAMEWORK; MULTIOBJECTIVE OPTIMIZATION;

OPTIMIZATION;

EID: 0036747856     PISSN: 1615147X     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00158-002-0224-0     Document Type: Article

References (27)

1. Arora, J.S. 1989: Introduction to optimum design. New York: McGraw-Hill.
2. Alexandrov, N.M.; Dennis, J.E. Jnr.; Lewis, R.M.; Torczon, V. 1998: A trust region framework for managing the use of approximation models in optimization. Struct. Optim. 15, 16-23
3. Alexandrov, N.M.; Lewis, R.M.; Gumbert, C.R.; Green, L.L.; Newman P.A. 1999: Optimization with variable-fidelity models applied to wing design. Technical Report No. 99-49, ICASE, Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, Hampton, VA
4. Booker, A.J.; Dennis, J.E. Jnr.; Frank P.D.; Serafini, D.B.; Torczon, V.; Trosset, M.W. 1999: A rigorous framework for optimization of expensive functions by surrogates. Struct. Optim. 17, 1-13
5. Burgee, S.; Giunta, A.A.; Balabanov, V.; Grossman, B.; Mason, W.H.; Narducci, R.; Haftka, R.T.; Watson, L.T. 1996: A coarse grained parallel variable-complexity multidisciplinary optimization paradigm. Int. J. Supercomputing Appl. High Perf. Computing 10, 269-299.
6. Cressie, N.A.C. 1993: Statistics for spatial data. New York: John Wiley & Sons.
7. Dennis, J.E.; Torczon Jr, V. 1997: Managing approximation models in optimization. In: Alexandrov, N.; Hussaini, M.Y. (eds.) Multidisciplinary design optimization: state-of-the-art, pp. 330-347. Philadelphia: SIAM. Also available as Technical Report 95-19, Department of Computational and Applied mathematics, Rice University, Houston, Texas 77005-1892.
8. Fonseca, C.M.; Fleming, P.J. 1993: Genetic algorithms for multiobjective optimization: formulation, discussion and generalization. Proc. 5-th Int. Conf. on Genetic Algorithms (held in San Mateo, CA), pp. 416-423. Morgan Kaufmann Publishers
9. Giunta, A.A. 1997: Aircraft multidisciplinary design optimization using design of experiments theory and response surface modeling methods. Ph.D. Thesis, Department of Aerospace and Ocean Engineering, Virginia Polytechnic Institute & State University, Blacksburg, VA. Also available as MAD 97-05-01, Multdisciplinary Analysis and Design Center for Advanced Vehicles, Virginia Polytechnic Institute & State University, Blacksburg, VA.
10. Goldberg, D.E. 1989: Genetic algorithms on search, optimization, and machine learning. Reading, MA: Addison-Wesley
11. Horn, J.; Nafpliotis, N.; Goldberg, D.E. 1994: A niched Pareto genetic algorithm for multiobjective optimization. Proc. 1st IEEE Conf. on Evolutionary Computation, Vol. 1, pp. 82-87. New York: Inst. of Electrical and Electronics Engineer
12. Johnson, M.E.; Moore, L.M.; Ylvisaker, D. 1990: Minmax and maximim distance design. J. Statistical Planning and Inference 26, 131-148
13. Kim, G.H. 1994: Multicriteria structural optimization by genetic algorithm (in Korean). Ph.D. Thesis. Department of Naval Architecture & Ocean Engineering. Seoul National University
14. Koch, P.N.; Allen, J.K.; Mistree, F.; Mavris, D. 1997: The problem of size in robust design. Proc. DETC'97 (ASME Design Engineering Technical Conf., Sacramento, CA), ASME Paper No. DETC97/DAC-3983
15. Mock, J. 1989: Bayesian approach to global optimization. Dordrecht: Kluwer
16. Nair, P.B.; Keane, A.J.; Shimpi, R.P. 1998: Combining approximation concepts with genetic algorithm-based structural optimization procedures. Proc. 39-th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conf. (held in Long Beach, CA), pp. 1741-1751, AIAA-98-1912
17. Osyczka, A. 1985: Multicriteria optimization for engineering design. In: Gero, J.S. (ed.) Design optimization, pp. 193-227. Orlando: Academic Press
18. Rasmussen, J. 1990: Structural optimization by accumulated function approximation. Report No. 20, Institute of Mechanical Engineering, Aalborg University, Denmark
19. Rodriguez, J.; Renaud, J.E.; Watson, L.T. 1997: Trust region augmented Lagrangian methods for sequential response surface approximation and optimization. Proc. DETC'97 (ASME Design Engineering Technical Conf., held in Sacramento, CA), ASME Paper No. DETC97/DAC3773
20. Ruy, W.S.; Yang, Y.S.; Kim, G.H.; Yeun, Y.S. 2001: Topology Design of Truss Structures in a Multicriteria Environment. Computer-Aided Civil and Infrastructure Engineering 16, 246-258
21. Shaffer, J.D. 1985: Multiple objective optimization with vector evaluated genetic algorithms. Proc. 1-st Int. Conf. on Genetic Algorithms (held in Pittsburgh, PA), pp. 93-100. Lawrence Erlbaum Assoc.
22. Simpson, T.W. 1998a: A concept exploration method for product family design. Ph.D. Thesis, Department of Mechanical Engineering, Georgia Institute of Technology
23. Simpson, T.W. 1998b: Spatial correlation metamodels for global approximation in structural design optimization. Proc. DETC98 (ASME Design Engineering Technical Conf., held Atlanta, GA)
24. Toropov, V.V. 1989: Simulation approach to structural optimization. Struct. Optim. 1, 37-46
25. Toropov, V.; van Keulen, F.; Markine, V.; de Boer, H. 1996: Refinements in the multi-point approximation method to reduce the effects of noisy structural responses. Proc. 6th AIAA/USAF/NASA/ISSMO Symp. on Multidisciplinary Analysis and Optimization (held in Bellevue, WA), Vol. 2, pp. 941-951 AIAA-96-4087-CP.
26. Trosset, M.W.; Torczon, V. 1997: Numerical optimization using computer experiments. Technical Report 97-38, ICASE, NASA Langly Research Center, Hampton, VA
27. Yang, Y.S.; Jang, B.S.; Yeun, Y.S.; Jung, H.S. 2000: A framework for managing approximation models in place of expensive simulations in optimization Proc. 2nd ASMO UK/ISSMO Conf. on Engineering Design Optimization (in Swansea, Wales), pp. 249-256