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Multilevel modelling for engineering design optimization

Published online by Cambridge University Press:  27 February 2009

Thomas Ellman
Affiliation:
Department of Computer Science, Hill Center for Mathematical Sciences, Rutgers University, Piscataway, NJ 08855, U.S.A.
John Keane
Affiliation:
Department of Computer Science, Hill Center for Mathematical Sciences, Rutgers University, Piscataway, NJ 08855, U.S.A.
Mark Schwabacher
Affiliation:
Department of Computer Science, Hill Center for Mathematical Sciences, Rutgers University, Piscataway, NJ 08855, U.S.A.
Ke-Thia Yao
Affiliation:
Department of Computer Science, Hill Center for Mathematical Sciences, Rutgers University, Piscataway, NJ 08855, U.S.A.
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Abstract

Physical systems can be modelled at many levels of approximation. The right model depends on the problem to be solved. In many cases, a combination of models will be more effective than a single model. Our research investigates this idea in the context of engineering design optimization. We present a family of strategies that use multiple models for unconstrained optimization of engineering designs. The strategies are useful when multiple approximations of an objective function can be implemented by compositional modelling techniques. We show how a compositional modelling library can be used to construct a variety of locally calibratable approximation schemes that can be incorporated into the optimization strategies. We analyze the optimization strategies and approximation schemes to formulate and prove sufficient conditions for correctness and convergence. We also report experimental tests of our methods in the domain of sailing yacht design. Our results demonstrate dramatic reductions in the CPU time required for optimization, on the problems we tested, with no significant loss in design quality.

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Copyright © Cambridge University Press 1997

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