Soft computing methodologies for structural optimization

Papadrakakis, M; Lagaros, ND

dc.contributor.author	Papadrakakis, M	en
dc.contributor.author	Lagaros, ND	en
dc.date.accessioned	2014-03-01T01:19:33Z
dc.date.available	2014-03-01T01:19:33Z
dc.date.issued	2003	en
dc.identifier.issn	15684946	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/15561
dc.subject	Evolution strategies	en
dc.subject	Monte Carlo simulation	en
dc.subject	Neural networks	en
dc.subject	Parallel computations	en
dc.subject	Reliability analysis	en
dc.subject	Structural optimization	en
dc.subject.other	Evolutionary algorithms	en
dc.subject.other	Finite element method	en
dc.subject.other	Monte Carlo methods	en
dc.subject.other	Neural networks	en
dc.subject.other	Problem solving	en
dc.subject.other	Reliability	en
dc.subject.other	Structural analysis	en
dc.subject.other	Computational efficiency	en
dc.subject.other	Evolution strategies	en
dc.subject.other	Parallel computations	en
dc.subject.other	Reliability analysis	en
dc.subject.other	Structural optimization	en
dc.title	Soft computing methodologies for structural optimization	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/S1568-4946(03)00040-1	en
heal.identifier.secondary	http://dx.doi.org/10.1016/S1568-4946(03)00040-1	en
heal.publicationDate	2003	en
heal.abstract	The paper examines the efficiency of soft computing techniques in structural optimization, in particular algorithms based on evolution strategies combined with neural networks, for solving large-scale, continuous or discrete structural optimization problems. The proposed combined algorithms are implemented both in deterministic and reliability based structural optimization problems, in an effort to increase the computational efficiency as well as the robustness of the optimization procedure. The use of neural networks was motivated by the time-consuming repeated finite element analyses required during the optimization process. A trained neural network is used to perform either the deterministic constraints check or, in the case of reliability based optimization, both the deterministic and the probabilistic constraints checks. The suitability of the neural network predictions is investigated in a number of structural optimization problems in order to demonstrate the computational advantages of the proposed methodologies. © 2003 Elsevier B.V. All rights reserved.	en
heal.journalName	Applied Soft Computing Journal	en
dc.identifier.doi	10.1016/S1568-4946(03)00040-1	en
dc.identifier.volume	3	en
dc.identifier.issue	3	en
dc.identifier.spage	283	en
dc.identifier.epage	300	en