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Structural and Multidisciplinary Optimization

Erschienen in:

05.07.2017 | RESEARCH PAPER

Ensemble of metamodels: extensions of the least squares approach to efficient global optimization

verfasst von: Wallace G. Ferreira, Alberto L. Serpa

Erschienen in: Structural and Multidisciplinary Optimization | Ausgabe 1/2018

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Abstract

In this work we present LSEGO, an approach to drive efficient global optimization (EGO), based on LS (least squares) ensemble of metamodels. By means of LS ensemble of metamodels it is possible to estimate the uncertainty of the prediction with any kind of model (not only kriging) and provide an estimate for the expected improvement function. For the problems studied, the proposed LSEGO algorithm has shown to be able to find the global optimum with less number of optimization cycles than required by the classical EGO approach. As more infill points are added per cycle, the faster is the convergence to the global optimum (exploitation) and also the quality improvement of the metamodel in the design space (exploration), specially as the number of variables increases, when the standard single point EGO can be quite slow to reach the optimum. LSEGO has shown to be a feasible option to drive EGO with ensemble of metamodels as well as for constrained problems, and it is not restricted to kriging and to a single infill point per optimization cycle.

Vorheriger Artikel Shape optimization with the level-set-method using local volume constraints

Nächster Artikel Multi-material topology optimization with multiple volume constraints: a general approach applied to ground structures with material nonlinearity

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Titel: Ensemble of metamodels: extensions of the least squares approach to efficient global optimization
verfasst von: Wallace G. Ferreira
Alberto L. Serpa
Publikationsdatum: 05.07.2017
Verlag: Springer Berlin Heidelberg
Erschienen in: Structural and Multidisciplinary Optimization / Ausgabe 1/2018
Print ISSN: 1615-147X
Elektronische ISSN: 1615-1488
DOI: https://doi.org/10.1007/s00158-017-1745-x

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