NDLI: Optimization on Metamodeling-Supported Iterative Decomposition

Content Provider	The American Society of Mechanical Engineers (ASME) Digital Collection
Author	Hajikolaei, Kambiz Haji Cheng, George Wang, Gary
Copyright Year	2015
Abstract	The recently developed metamodel-based decomposition strategy relies on quantifying the variable correlations of black-box functions so that high dimensional problems are decomposed to smaller sub-problems, before performing optimization. Such a two-step method may miss the global optimum due to its rigidity or requires extra expensive sample points for ensuring adequate decomposition. This work develops a strategy to iteratively decompose high dimensional problems within the optimization process. The sample points used during the optimization are reused to build a metamodel called PCA-HDMR for quantifying the intensities of variable correlations by sensitivity analysis. At every iteration, the predicted intensities of the correlations are updated based on all the evaluated points and a new decomposition scheme is suggested by omitting the weak correlations. Optimization is performed on the iteratively updated sub-problems from decomposition. The proposed strategy is applied for optimization of different benchmark and engineering problems and results are compared to direct optimization of the undecomposed problems using Trust Region Mode Pursuing Sampling method (TRMPS), Genetic Algorithm (GA), and Dividing RECTangles (DIRECT). The results show that except for the category of un-decomposable problems with all or lots of strong (i. e., important) correlations, the proposed strategy effectively improves the accuracy of the optimization results. The advantages of the new strategy in comparison with the previous methods are also discussed.
Sponsorship	Design Engineering Division Computers and Information in Engineering Division
File Format	PDF
ISBN	9780791857083
DOI	10.1115/DETC2015-47525
Volume Number	Volume 2B: 41st Design Automation Conference
Conference Proceedings	ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
Language	English
Publisher Date	2015-08-02
Publisher Place	Boston, Massachusetts, USA
Access Restriction	Subscribed
Subject Keyword	Sampling methods Stiffness Sensitivity analysis Optimization Genetic algorithms
Content Type	Text
Resource Type	Article

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