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Optimization of large-scale 3-D trusses using evolution strategies and neural network

Manolis padrakakis , Nikos Lagaros , Yiannis Tsompanakis

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URIhttp://purl.tuc.gr/dl/dias/574B6DA9-8769-46B1-8399-FBF07359A665-
Identifierhttps://doi.org/10.1260/0266351991494830-
Languageen-
Extent12 pagesen
TitleOptimization of large-scale 3-D trusses using evolution strategies and neural networken
CreatorManolis padrakakis en
CreatorNikos Lagaros en
CreatorYiannis Tsompanakis en
Content SummaryThe objective of this paper is to investigate the efficiency of optimization algorithms, based on evolution strategies, for the solution of large-scale structural optimization problems. Furthermore, the structural analysis phase is replaced by a neural network prediction for the computation of the necessary data for the evolution strategies (ES) optimization procedure. The use of neural networks (NN) was motivated by the time-consuming repeated analyses required by ES during the optimization process. A back propagation algorithm is implemented for training the NN using data derived from selected analyses. The trained NN is then used to predict, within an acceptable accuracy, the values of the objective and constraint functions. The proposed methodology has been applied in sizing structural optimization problems of large-scale three dimensional roof trusses. The numerical tests presented demonstrate the computational advantages of the proposed approach which become more pronounced for large-scale optimization problems. en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-10-08-
Date of Publication1999-
SubjectGreek mathematicsen
Subjectmathematics greeken
Subjectgreek mathematicsen
Bibliographic CitationM. Papadrakakis ,N. Lagaros , Y. Tsompanakis , " Optimization of large-scale 3-D trusses using evolution strategies and neural network ,"Intern. J.of Space Str. ,vol.14 ,no. 3 ,pp. 211-223,1999. doi: 10.1260/0266351991494830en

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