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Optimization and Engineering

Erschienen in:

02.09.2015

Dynamic scaling in the mesh adaptive direct search algorithm for blackbox optimization

verfasst von: Charles Audet, Sébastien Le Digabel, Christophe Tribes

Erschienen in: Optimization and Engineering | Ausgabe 2/2016

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Abstract

Blackbox optimization deals with situations in which the objective function and constraints are typically computed by launching a time-consuming computer simulation. The subject of this work is the mesh adaptive direct search (mads) class of algorithms for blackbox optimization. We propose a way to dynamically scale the mesh, which is the discrete spatial structure on which mads relies, so that it automatically adapts to the characteristics of the problem to solve. Another objective of the paper is to revisit the mads method in order to ease its presentation and to reflect recent developments. This new presentation includes a nonsmooth convergence analysis. Finally, numerical tests are conducted to illustrate the efficiency of the dynamic scaling, both on academic test problems and on a supersonic business jet design problem.

Vorheriger Artikel Data-driven construction of Convex Region Surrogate models

Nächster Artikel A matrix-free augmented lagrangian algorithm with application to large-scale structural design optimization

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Our numerical experiments show that the performances of MADS 2N(iso) and the algorithm in Abramson et al. (2009) are practically identical.

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Titel: Dynamic scaling in the mesh adaptive direct search algorithm for blackbox optimization
verfasst von: Charles Audet
Sébastien Le Digabel
Christophe Tribes
Publikationsdatum: 02.09.2015
Verlag: Springer US
Erschienen in: Optimization and Engineering / Ausgabe 2/2016
Print ISSN: 1389-4420
Elektronische ISSN: 1573-2924
DOI: https://doi.org/10.1007/s11081-015-9283-0

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