Structural optimization using evolution strategies and neural networks
type: Item
instance of: scholarly article
title: Structural optimization using evolution strategies and neural networks
zbMATH Open document ID: 0964.74045
DOI: 10.1016/S0045-7825(97)00215-6
author: M. Papadrakakis
author: N. D. Lagaros
author: Y. Tsompanakis
published in: Computer Methods in Applied Mechanics and Engineering
publication date: 2001-07-22T00:00:00Z
review text: The authors attack structural optimization problems by means of a combination of evolution strategies (ES) and artificial neural networks (ANN). Since the computation of objective and constraint functions of real world problems may be time-consuming, these functions are simulated by means of an ANN. The latter is trained using specified data and a ``feedforward error back propagation'' training procedure. The versions of ES proposed by \textit{H.-P. Schwefel} [Numerische Optimierung von Computer-Modellen mittels der Evolutionsstrategie. Mit einer vergleichenden Einführung in die Hill-Climbing- und Zufallsstrategie. Interdisciplinar Systems Research. 26. Basel-Stuttgart: Birkhäuser Verlag (1977; Zbl 0347.65035)] are applied. The authors give an extensive introduction into ES and AN, and present several examples of application of the described method.
Mathematics Subject Classification ID: 74P10
Mathematics Subject Classification ID: 74S20
Mathematics Subject Classification ID: 92B20
zbMATH DE Number: 1422700
zbMATH Keywords: optimal design
zbMATH Keywords: objective function
zbMATH Keywords: feedforward error back propagation
zbMATH Keywords: structural optimization
zbMATH Keywords: evolution strategies
zbMATH Keywords: artificial neural networks
zbMATH Keywords: constraint functions
zbMATH Keywords: training procedure
Wikidata QID: Q57577858
reviewed by: Antanas Žilinskas
MaRDI profile type: Publication
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full work available at URL: s0045-7825(97)00215-6
OpenAlex ID: W2065273135
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description: scientific article; zbMATH DE number 1422700