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

Asouti, VG; Kampolis, IC; Giannakoglou, KC

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	https://dspace.lib.ntua.gr/xmlui/handle/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