Soft computing methodologies for structural optimization

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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 http://hdl.handle.net/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

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