An adaptive surrogate-assisted particle swarm optimization for expensive problems

Abstract To solve engineering problems with evolutionary algorithms, many expensive function evaluations (FEs) are required. To alleviate this difficulty, surrogate-assisted evolutionary algorithms (SAEAs) have attracted increasingly more attention in both academia and industry. Most existing SAEAs...
Ausführliche Beschreibung

Gespeichert in:
Autor*in:

Li, Xuemei [verfasserIn]

Li, Shaojun

Format:

Artikel

Sprache:

Englisch

Erschienen:

2021

Schlagwörter:

Surrogate-assisted evolutionary algorithm

Ensemble model

Radial basis functions

Particle swarm optimization

Anmerkung:

© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021

Übergeordnetes Werk:

Enthalten in: Soft computing - Springer Berlin Heidelberg, 1997, 25(2021), 24 vom: 08. Okt., Seite 15051-15065

Übergeordnetes Werk:

volume:25 ; year:2021 ; number:24 ; day:08 ; month:10 ; pages:15051-15065

Links:

Volltext

DOI / URN:

10.1007/s00500-021-06348-2

Katalog-ID:

OLC2077350555

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