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An asynchronous metamodel-assisted memetic algorithm for CFD-based shape optimization

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dc.contributor.author Kontoleontos, EA en
dc.contributor.author Asouti, VG en
dc.contributor.author Giannakoglou, KC en
dc.date.accessioned 2014-03-01T02:07:37Z
dc.date.available 2014-03-01T02:07:37Z
dc.date.issued 2012 en
dc.identifier.issn 0305215X en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/29594
dc.subject adjoint method en
dc.subject asynchronous metamodel-assisted evolutionary algorithm en
dc.subject computational fluid dynamics en
dc.subject memetic algorithm en
dc.subject shape optimization en
dc.subject.other Adjoint equations en
dc.subject.other Adjoint methods en
dc.subject.other CFD applications en
dc.subject.other Evaluation models en
dc.subject.other Gradient computation en
dc.subject.other Learning process en
dc.subject.other Local search en
dc.subject.other Local search method en
dc.subject.other Memetic algorithms en
dc.subject.other Multi-objective problem en
dc.subject.other Multi-processor platforms en
dc.subject.other Objective functions en
dc.subject.other Optimization problems en
dc.subject.other Computational fluid dynamics en
dc.subject.other Shape optimization en
dc.subject.other Evolutionary algorithms en
dc.title An asynchronous metamodel-assisted memetic algorithm for CFD-based shape optimization en
heal.type journalArticle en
heal.identifier.primary 10.1080/0305215X.2011.570758 en
heal.identifier.secondary http://dx.doi.org/10.1080/0305215X.2011.570758 en
heal.publicationDate 2012 en
heal.abstract This article presents an asynchronous metamodel-assisted memetic algorithm for the solution of CFD-based optimization problems. This algorithm is appropriate for use on multiprocessor platforms and may solve computationally expensive optimization problems in reduced wall-clock time, compared to conventional evolutionary or memetic algorithms. It is, in fact, a hybridization of non-generation-based (asynchronous) evolutionary algorithms, assisted by surrogate evaluation models, a local search method and the Lamarckian learning process. For the objective function gradient computation, in CFD applications, the adjoint method is used. Issues concerning the smart implementation of local search in multi-objective problems are discussed. In this respect, an algorithmic scheme for reducing the number of calls to the adjoint equations to just one, irrespective of the number of objectives, is proposed. The algorithm is applied to the CFD-based shape optimization of the tubes of a heat exchanger and of a turbomachinery cascade. © 2012 Taylor and Francis Group, LLC. en
heal.journalName Engineering Optimization en
dc.identifier.doi 10.1080/0305215X.2011.570758 en
dc.identifier.volume 44 en
dc.identifier.issue 2 en
dc.identifier.spage 157 en
dc.identifier.epage 173 en


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