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Intelligent gradient-based search of incompletely defined design spaces

Published online by Cambridge University Press:  27 February 2009

Mark Schwabacher  and
Andrew Gelsey
Mark Schwabacher
Affiliation:
National Institute of Standards and Technology, Building 304, Room 12, Gaithersburg, MD 20899, U.S.A.
Andrew Gelsey
Affiliation:
Rutgers, the State University of New Jersey, New Brunswick, NJ 08903, U.S.A.
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Abstract

Gradient-based numerical optimization of complex engineering designs offers the promise of rapidly producing better designs. However, such methods generally assume that the objective function and constraint functions are continuous, smooth, and defined everywhere. Unfortunately, realistic simulators tend to violate these assumptions. We present a rule-based technique for intelligently computing gradients in the presence of such pathologies in the simulators, and show how this gradient computation method can be used as part of a gradient-based numerical optimization system. We tested the resulting system in the domain of conceptual design of supersonic transport aircraft, and found that using rule-based gradients can decrease the cost of design space search by one or more orders of magnitude.

Keywords

OptimizationGradientsSequential Quadratic ProgrammingRule-based Systems
Type
Articles
Information
AI EDAM , Volume 11 , Issue 3 , June 1997 , pp. 199 - 210
Copyright
Copyright © Cambridge University Press 1997

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References

REFERENCES

Bouchard, E.E., Kidwell, G.H., & Rogan, J.E. (1988). The application of artificial intelligence technology to aeronautical system design. AIAA/AHS/ASEE Aircraft Design Systems and Operations Meeting, AIAA Paper 88–4426.CrossRefGoogle Scholar
Bramlette, M., Bouchard, E., Buckman, E., & Takacs, L. (1990). Current applications of genetic algorithms to aeronautical systems. Proc. Sixth Ann. Aerospace Applications Artif. Intell. Conf.Google Scholar
Ellman, T., Keane, J., & Schwabacher, M. (1993). Intelligent model selection for hillclimbing search in computer-aided design. Proc. Eleventh Nat. Conf. Artif. Intell., 594–599.Google Scholar
Forbus, K.D., & Falkenhainer, B. (1990). Self-explanatory simulations: An integration of qualitative and quantitative knowledge. Proc. Eighth Nat. Conf. Artif. Intell., 380–387.Google Scholar
Forbus, K.D., & Falkenhainer, B. (1992). Self-explanatory simulations: Scaling up to large models. Proc. Tenth Nat. Conf. Artif. Intell.Google Scholar
Forbus, K.D., & Falkenhainer, B. (1995). Scaling up self-explanatory simulations: Polynomial-time compilation. Proc. Fourteenth Int. Joint Conf. Artif. Intell., 1798–1805.Google Scholar
Gage, P. (1994). New approaches to optimization in aerospace conceptual design. PhD Thesis. Stanford University, Stanford, CA.Google Scholar
Gage, P., Kroo, I., & Sobieski, I. (1995). Variable-complexity genetic algorithm for topological design. AIAA Journal 33(11), 2212–2217.CrossRefGoogle Scholar
Gelsey, A., & Smith, D. (1996). Computational environment for exhaust nozzle design. J. Aircraft 33(3), 470–476.CrossRefGoogle Scholar
Gelsey, A. (1995). Intelligent automated quality control for computational simulation. Al EDAM 9(5), 387–400.Google Scholar
Gelsey, A., Knight, D.D., Gao, S., & Schwabacher, M. (1995). NPARC simulation and redesign of the NASA P2 hypersonic inlet. Thirty-first Joint Propulsion Conference, AIAA Paper 95–2760.CrossRefGoogle Scholar
Gelsey, A., Smith, D., Schwabacher, M., Rasheed, K., & Miyake, K. (1996 a). A search space toolkit: SST. Decis. Support Sys. 18, 341–356.CrossRefGoogle Scholar
Gelsey, A., Schwabacher, M., & Smith, D. (1996 b). Using modeling knowledge to guide design space search. In Artificial Intelligence in Design '96, (Gero, J.S. and Sudweeks, F. Eds.), pp. 367–385. Kluwer Academic Publishers, The Netherlands.Google Scholar
Goldberg, D.E. (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading, Massachusetts.Google Scholar
Hoeltzel, D., & Chieng, W. (1987). Statistical machine learning for the cognitive selection of nonlinear programming algorithms in engineering design optimization. In Advances in Design Automation. Boston, Massachusetts.Google Scholar
Kroo, I., Altus, S., Braun, R., Gage, P., & Sobieski, I. (1994). Multidisciplinary optimization methods for aircraft preliminary design. Fifth AIAA/USAF/NASA/ISSMO Symp. MultidisciplinaryAnalysis and Optimization, AIAA Paper 94–4325.CrossRefGoogle Scholar
Lawrence, C., Zhou, J., & Tits, A. (1995). User's guide for CFSQP version 2.3: A C code for solving (large scale) constraned nonlinear (minimax) optimization problems, generating iterates satisfying all inequality constraints. Technical Report No. TR-94–16r1. Institute for Systems Research, University of Maryland, CollegePark, MD.Google Scholar
Orelup, M.F., Dixon, J.R., Cohen, P.R., & Simmons, M.K. (1988). Dominic II: Meta-level control in iterative redesign. Proc. Nat. Conf. Artif. Intell. pp. 25–30.Google Scholar
Powell, D. (1990). Inter-GEN: A hybrid approach to engineering design optimization. PhD Dissertation, Rensselaer Polytechnic Institute, Department of Computer Science, Troy, NY.Google Scholar
Powell, D., & Skolnick, M. (1993). Using genetic algorithms in engineering design optimization with non-linear constraints. Proc. Fifth Int. Conf. Genetic Algorithms, pp. 424–431. Morgan Kaufmann, Los Altos, California.Google Scholar
Press, W., Flannery, B., Teukolsky, S., & Vetterling, W. (1986). Numerical recipes. Cambridge University Press, New York.Google Scholar
Schwabacher, M. (1996). The use of artificial intelligence to improve the numerical optimization of complex engineering designs. PhD Dissertation. Rutgers University Department of Computer Science, New Brunswick, NJ.Google Scholar
Schwabacher, M., Hirsh, H., & Ellman, T. (1994). Learning prototype-selection rules for case-based iterative design. Proc. Tenth IEEE Conf. Artif. Intell. Applications, 56–62.CrossRefGoogle Scholar
Schwabacher, M., Ellman, T., Hirsh, H., & Richter, G. (1996). Learning to choose a reformulation for numerical optimization of engineering designs. In Artificial Intelligence in Design '96, (Gero, J.S. and Sudweeks, F., Eds.), pp. 447–462. Kluwer Academic Publishers, The Netherlands.Google Scholar
Shukla, V., Gelsey, A., Schwabacher, M., Smith, D., & Knight, D.D. (1996). Automated redesign of the NASA P8 hypersonic inlet using numerical optimization. AIAA Joint Propulsion Conf. AIAA Paper 96–2549.CrossRefGoogle Scholar
Shukla, V., Gelsey, A., Schwabacher, M., Smith, D., & Knight, D.D. (1997). Automated design optimization for the P2 and P8 hypersonic inlets. AIAA J. Aircraft 34(2), 228–235.CrossRefGoogle Scholar
Sobieszczanski-Sobieski, J., & Haftka, R.T. (1996). Multidisciplinary aerospace design optimization: Survey of recent developments. 34th AIAA Aerospace Sciences Meeting and Exhibit. AIAA Paper 96–0711.CrossRefGoogle Scholar
Sobieszczanski-Sobieski, J., James, B.B., & Dovi, A.R. (1985). Structural optimization by multilevel decomposition. AIAA Journal 23(11), 1775–1782.CrossRefGoogle Scholar
Tong, S.S. (1988). Coupling symbolic manipulation and numerical simulation for complex engineering designs. Int. Assoc. Math. Computers in Simulation Conf. Expert Syst. Numerical Computing.Google Scholar
Tong, S.S., Powell, D., & Goel, S. (1992). Integration of artificial intelligence and numerical optimization techniques for the design of complex aerospace systems. 1992 Aerospace Design Conf. AIAA Paper 92–1189.CrossRefGoogle Scholar
Zha, G.-C., Smith, D., Schwabacher, M., Gelsey, A., & Knight, D. (1996). High performance supersonic missile inlet design using automated optimization. Sixth AIAA/NASA/USAF Multidisciplinary Analysis & Optimization Symp., 1355–1371.CrossRefGoogle Scholar
Zha, G.-C., Smith, D., Schwabacher, M., Gelsey, A., & Knight, D. (1997). High performance supersonic missile inlet design using automated optimization. AIAA J. Aircraft (to appear).CrossRefGoogle Scholar