Analysis of multi-objective Kriging-based methods for constrained global optimization

Cédric Durantin (), Julien Marzat () and Mathieu Balesdent ()

Computational Optimization and Applications, 2016, vol. 63, issue 3, 903-926

Abstract: Metamodeling, i.e., building surrogate models to expensive black-box functions, is an interesting way to reduce the computational burden for optimization purpose. Kriging is a popular metamodel based on Gaussian process theory, whose statistical properties have been exploited to build efficient global optimization algorithms. Single and multi-objective extensions have been proposed to deal with constrained optimization when the constraints are also evaluated numerically. This paper first compares these methods on a representative analytical benchmark. A new multi-objective approach is then proposed to also take into account the prediction accuracy of the constraints. A numerical evaluation is provided on the same analytical benchmark and a realistic aerospace case study. Copyright Springer Science+Business Media New York 2016

Keywords: Black-box functions; Constrained global optimization; Kriging; Multi-objective optimization (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s10589-015-9789-6

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