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A NOVEL METHOD FOR REFINING A META-MODEL BY PARETO FRONTIER  

Jo, S.J. (부산대학교 대학원 항공우주공학과)
Chae, S.H. (부산대학교 대학원 항공우주공학과)
Yee, K.J. (부산대학교)
Publication Information
Journal of computational fluids engineering / v.14, no.4, 2009 , pp. 31-40 More about this Journal
Abstract
Although optimization by sequentially refining metamodels is known to be computationally very efficient, the metamodel that can be used for this purpose is limited to Kriging method due to the difficulties related with sample points selections. The present study suggests a novel method for sequentially refining metamodels using Pareto Frontiers, which can be used independent of the type of metamodels. It is shown from the examples that the present method yields more accurate metamodels compared with full-factorial optimization and also guarantees global optimum irrespective of the initial conditions. Finally, in order to prove the generality of the present method, it is applied to a 2D transonic airfoil optimization problem, and the successful design results are obtained.
Keywords
Genetic Algorithm; Artificial Neural Network; Pareto Frontier; Kriging; Metamodel; Selective Sampling;
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