Distributed evolutionary algorithms with hierarchical evaluation

Kampolis, IC; Giannakoglou, KC

dc.contributor.author	Kampolis, IC	en
dc.contributor.author	Giannakoglou, KC	en
dc.date.accessioned	2014-03-01T01:30:13Z
dc.date.available	2014-03-01T01:30:13Z
dc.date.issued	2009	en
dc.identifier.issn	0305-215X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19503
dc.subject	Design optimization	en
dc.subject	Distributed search	en
dc.subject	Evolutionary algorithms	en
dc.subject	Hierarchical search	en
dc.subject	Metamodels	en
dc.subject.classification	Engineering, Multidisciplinary	en
dc.subject.classification	Operations Research & Management Science	en
dc.subject.other	Airfoil design	en
dc.subject.other	Compressor cascade	en
dc.subject.other	Computational costs	en
dc.subject.other	Design optimization	en
dc.subject.other	Distributed evolutionary algorithms	en
dc.subject.other	Distributed schemes	en
dc.subject.other	Distributed search	en
dc.subject.other	Engineering optimization problems	en
dc.subject.other	Evaluation models	en
dc.subject.other	Hierarchical evaluation	en
dc.subject.other	Hierarchical search	en
dc.subject.other	High fidelity	en
dc.subject.other	Meta model	en
dc.subject.other	Metamodels	en
dc.subject.other	Surrogate model	en
dc.subject.other	Airfoils	en
dc.subject.other	Computational efficiency	en
dc.subject.other	Cost reduction	en
dc.subject.other	Optimization	en
dc.subject.other	Evolutionary algorithms	en
dc.title	Distributed evolutionary algorithms with hierarchical evaluation	en
heal.type	journalArticle	en
heal.identifier.primary	10.1080/03052150902890072	en
heal.identifier.secondary	http://dx.doi.org/10.1080/03052150902890072	en
heal.language	English	en
heal.publicationDate	2009	en
heal.abstract	A distributed evolutionary algorithm is presented that is based on a hierarchy of (fitness or cost function) evaluation passes within each deme and is efficient in solving engineering optimization problems. Starting with non-problem-specific evaluations (using surrogate models or metamodels, trained on previously evaluated individuals) and ending up with high-fidelity problem-specific evaluations, intermediate passes rely on other available lower-fidelity problem-specific evaluations with lower CPU cost per evaluation. The sequential use of evaluation models or metamodels, of different computational cost and modelling accuracy, by screening the generation members to get rid of non-promising individuals, leads to reduced overall computational cost. The distributed scheme is based on loosely coupled demes that exchange regularly their best-so-far individuals. Emphasis is put on the optimal way of coupling distributed and hierarchical search methods. The proposed method is tested on mathematical and compressor cascade airfoil design problems. © 2009 Taylor & Francis.	en
heal.publisher	TAYLOR & FRANCIS LTD	en
heal.journalName	Engineering Optimization	en
dc.identifier.doi	10.1080/03052150902890072	en
dc.identifier.isi	ISI:000274363000003	en
dc.identifier.volume	41	en
dc.identifier.issue	11	en
dc.identifier.spage	1037	en
dc.identifier.epage	1049	en