An adaptive neural network strategy for improving the computational performance of evolutionary structural optimization

Lagaros, ND; Charmpis, DC; Papadrakakis, M

dc.contributor.author	Lagaros, ND	en
dc.contributor.author	Charmpis, DC	en
dc.contributor.author	Papadrakakis, M	en
dc.date.accessioned	2014-03-01T01:21:48Z
dc.date.available	2014-03-01T01:21:48Z
dc.date.issued	2005	en
dc.identifier.issn	0045-7825	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/16380
dc.subject	Domain decomposition	en
dc.subject	Evolution strategies	en
dc.subject	Neural networks	en
dc.subject	Optimization	en
dc.subject	Parallel computing	en
dc.subject.classification	Engineering, Multidisciplinary	en
dc.subject.classification	Mathematics, Interdisciplinary Applications	en
dc.subject.classification	Mechanics	en
dc.subject.other	Algorithms	en
dc.subject.other	Computational methods	en
dc.subject.other	Finite element method	en
dc.subject.other	Neural networks	en
dc.subject.other	Optimization	en
dc.subject.other	Reliability	en
dc.subject.other	Structural design	en
dc.subject.other	Design vectors	en
dc.subject.other	Displacement	en
dc.subject.other	Evolution strategies (ES)	en
dc.subject.other	Sequential computing	en
dc.subject.other	Structural analysis	en
dc.subject.other	optimization	en
dc.subject.other	parallel computing	en
dc.subject.other	structural performance	en
dc.title	An adaptive neural network strategy for improving the computational performance of evolutionary structural optimization	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.cma.2004.12.023	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.cma.2004.12.023	en
heal.language	English	en
heal.publicationDate	2005	en
heal.abstract	This work is focused on improving the computational efficiency of evolutionary algorithms implemented in large-scale structural optimization problems. Locating optimal structural designs using evolutionary algorithms is a task associated with high computational cost, since a complete finite element (FE) analysis needs to be carried out for each parent and offspring design vector of the populations considered. Each of these FE solutions facilitates decision making regarding the feasibility or infeasibility of the corresponding structural design by evaluating the displacement and stress constraints specified for the structural problem at hand. This paper presents a neural network (NN) strategy to reliably predict, in the framework of an evolution strategies (ES) procedure for structural optimization. the feasibility or infeasibility of structural designs avoiding computationally expensive FE analyses, The proposed NN implementation is adaptive in the sense that the utilized NN configuration is appropriately updated as the FS process evolves by performing NN retrainings using information gradually accumulated during the ES execution, The prediction capabilities and the computational advantages offered by this adaptive NN scheme coupled with domain decomposition solution techniques are investigated in the context of design optimization of skeletal structures On both sequential and parallel computing environments. (c) 2005 Elsevier B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE SA	en
heal.journalName	Computer Methods in Applied Mechanics and Engineering	en
dc.identifier.doi	10.1016/j.cma.2004.12.023	en
dc.identifier.isi	ISI:000230014800009	en
dc.identifier.volume	194	en
dc.identifier.issue	30-33 SPEC. ISS.	en
dc.identifier.spage	3374	en
dc.identifier.epage	3393	en