A grid-enabled asynchronous metamodel-assisted evolutionary algorithm for aerodynamic optimization

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dc.contributor.author Asouti, VG en
dc.contributor.author Kampolis, IC en
dc.contributor.author Giannakoglou, KC en
dc.date.accessioned 2014-03-01T01:29:33Z
dc.date.available 2014-03-01T01:29:33Z
dc.date.issued 2009 en
dc.identifier.issn 1389-2576 en
dc.identifier.uri http://hdl.handle.net/123456789/19301
dc.subject Aerodynamic shape optimization en
dc.subject Asynchronous evolutionary algorithms en
dc.subject Grid computing en
dc.subject Metamodels en
dc.subject.classification Computer Science, Artificial Intelligence en
dc.subject.classification Computer Science, Theory & Methods en
dc.subject.other Aerodynamic optimization en
dc.subject.other Aerodynamic shape optimization en
dc.subject.other Artificial Neural Network en
dc.subject.other Asynchronous evolutionary algorithms en
dc.subject.other Compressor cascade en
dc.subject.other Evaluation models en
dc.subject.other Globus Toolkit en
dc.subject.other Grid deployment en
dc.subject.other Meta model en
dc.subject.other Metamodels en
dc.subject.other Middleware layer en
dc.subject.other Multi objective en
dc.subject.other Pre-evaluation en
dc.subject.other Search method en
dc.subject.other Aerodynamics en
dc.subject.other Airfoils en
dc.subject.other Computation theory en
dc.subject.other Grid computing en
dc.subject.other Middleware en
dc.subject.other Neural networks en
dc.subject.other Shape optimization en
dc.subject.other Evolutionary algorithms en
dc.title A grid-enabled asynchronous metamodel-assisted evolutionary algorithm for aerodynamic optimization en
heal.type journalArticle en
heal.identifier.primary 10.1007/s10710-009-9090-5 en
heal.identifier.secondary http://dx.doi.org/10.1007/s10710-009-9090-5 en
heal.language English en
heal.publicationDate 2009 en
heal.abstract A Grid-enabled asynchronous metamodel-assisted evolutionary algorithm is presented and assessed on a number of aerodynamic shape optimization problems. An efficient way of implementing surrogate evaluation models or metamodels (artificial neural networks) in the context of an asynchronous evolutionary algorithm is proposed. The use of metamodels relies on the inexact pre-evaluation technique already successfully applied to synchronous (i.e. generation-based) evolutionary algorithms, which needs to be revisited so as to efficiently cooperate with the asynchronous search method. The so-created asynchronous metamodel-assisted evolutionary algorithm is further enabled for Grid Computing. The Grid deployment of the algorithm relies on three middleware layers: GridWay, Globus Toolkit and Condor. Single- and multi-objective CFD-based designs of isolated airfoils and compressor cascades are handled using the proposed algorithm and the gain in CPU cost is demonstrated. © 2009 Springer Science+Business Media, LLC. en
heal.publisher SPRINGER en
heal.journalName Genetic Programming and Evolvable Machines en
dc.identifier.doi 10.1007/s10710-009-9090-5 en
dc.identifier.isi ISI:000271404200003 en
dc.identifier.volume 10 en
dc.identifier.issue 4 en
dc.identifier.spage 373 en
dc.identifier.epage 389 en

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