<efrbr:recordSet xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:efrbr="http://vfrbr.info/efrbr/1.1" xmlns:efrbr-work="http://vfrbr.info/efrbr/1.1/work" xmlns:efrbr-expression="http://vfrbr.info/efrbr/1.1/expression" xmlns:efrbr-manifestation="http://vfrbr.info/efrbr/1.1/manifestation" xmlns:efrbr-person="http://vfrbr.info/efrbr/1.1/person" xmlns:efrbr-corporateBody="http://vfrbr.info/efrbr/1.1/corporateBody" xmlns:efrbr-concept="http://vfrbr.info/efrbr/1.1/concept" xmlns:efrbr-structure="http://vfrbr.info/efrbr/1.1/structure" xmlns:efrbr-responsible="http://vfrbr.info/efrbr/1.1/responsible" xmlns:efrbr-subject="http://vfrbr.info/efrbr/1.1/subject" xmlns:efrbr-other="http://vfrbr.info/efrbr/1.1/other" xsi:schemaLocation="http://vfrbr.info/efrbr/1.1 http://vfrbr.info/schemas/1.1/efrbr.xsd"><efrbr:entities><efrbr-work:work identifier="http://purl.tuc.gr/dl/dias/574B6DA9-8769-46B1-8399-FBF07359A665"><efrbr-work:titleOfTheWork>Optimization of large-scale 3-D trusses using evolution strategies and neural network</efrbr-work:titleOfTheWork></efrbr-work:work><efrbr-expression:expression identifier="http://purl.tuc.gr/dl/dias/574B6DA9-8769-46B1-8399-FBF07359A665"><efrbr-expression:titleOfTheExpression>Optimization of large-scale 3-D trusses using evolution strategies and neural network</efrbr-expression:titleOfTheExpression><efrbr-expression:formOfExpression vocabulary="DIAS:TYPES">
            Peer-Reviewed Journal Publication
            Δημοσίευση σε Περιοδικό με Κριτές
         </efrbr-expression:formOfExpression><efrbr-expression:dateOfExpression type="issued">2015-10-08</efrbr-expression:dateOfExpression><efrbr-expression:dateOfExpression type="published">1999</efrbr-expression:dateOfExpression><efrbr-expression:languageOfExpression vocabulary="iso639-1">en</efrbr-expression:languageOfExpression><efrbr-expression:summarizationOfContent>The 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. 

</efrbr-expression:summarizationOfContent><efrbr-expression:useRestrictionsOnTheExpression type="creative-commons">http://creativecommons.org/licenses/by/4.0/</efrbr-expression:useRestrictionsOnTheExpression><efrbr-expression:note type="journal name">International Journal of Space Structures</efrbr-expression:note><efrbr-expression:note type="journal volume">3</efrbr-expression:note><efrbr-expression:note type="journal number">14</efrbr-expression:note><efrbr-expression:note type="page range">211-223</efrbr-expression:note></efrbr-expression:expression><efrbr-person:person identifier="AA47016C-DB69-4F62-B099-16678E9C8B01"><efrbr-person:nameOfPerson vocabulary="">
            Manolis padrakakis 
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            Nikos Lagaros 
         </efrbr-person:nameOfPerson></efrbr-person:person><efrbr-person:person identifier="43109827-89A7-42B2-AFAE-32D03F019997"><efrbr-person:nameOfPerson vocabulary="">
            Yiannis Tsompanakis  
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            Greek mathematics
            mathematics greek
            greek mathematics
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