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

Kontoleontos, EA; Asouti, VG; Giannakoglou, KC

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