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Surrogate-Based Optimization using Multifidelity Models with Variable Parameterization

By T. D. Robinson, M. S. Eldred, K. E. Willcox and R. Haimes

Abstract

Surrogate-based-optimization methods provide a means to achieve high-fidelity design optimization at reduced computational cost by using a high-fidelity model in combination with lower-fidelity models that are less expensive to evaluate. This paper presents a provably convergent trust-region model-management methodology for variableparameterization design models: that is, models for which the design parameters are defined over different spaces. Corrected space mapping is introduced as a method to map between the variable-parameterization design spaces. It is then used with a sequential-quadratic-programming-like trust-region method for two aerospace-related design optimization problems. Results for a wing design problem and a flapping-flight problem show that the method outperforms direct optimization in the high-fidelity space. On the wing design problem, the new method achieves 76 % savings in high-fidelity function calls. On a bat-flight design problem, it achieves approximately 45 % time savings, although it converges to a different local minimum than did the benchmark

Topics: AIAA. Principal Member of the Technical Staff, Optimization and Uncertainty
Year: 2007
OAI identifier: oai:CiteSeerX.psu:10.1.1.352.6845
Provided by: CiteSeerX
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