A dynamic surrogate-assisted evolutionary algorithm framework for expensive structural optimization

Abstract In the expensive structural optimization, the data-driven surrogate model has been proven to be an effective alternative to physical simulation (or experiment). However, the static surrogate-assisted evolutionary algorithm (SAEA) often becomes powerless and inefficient when dealing with dif...
Ausführliche Beschreibung

Gespeichert in:
Autor*in:

Yu, Mingyuan [verfasserIn]

Li, Xia

Liang, Jing

Format:

Artikel

Sprache:

Englisch

Erschienen:

2019

Schlagwörter:

Evolutionary algorithm

Adaptive surrogate model

Expensive optimization

Reliability

Anmerkung:

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Übergeordnetes Werk:

Enthalten in: Structural and multidisciplinary optimization - Springer Berlin Heidelberg, 2000, 61(2019), 2 vom: 07. Nov., Seite 711-729

Übergeordnetes Werk:

volume:61 ; year:2019 ; number:2 ; day:07 ; month:11 ; pages:711-729

Links:

Volltext

DOI / URN:

10.1007/s00158-019-02391-8

Katalog-ID:

OLC2051791325

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