Hierarchical distributed metamodel-assisted evolutionary algorithms in shape optimization

Karakasis, MK; Koubogiannis, DG; Giannakoglou, KC

dc.contributor.author	Karakasis, MK	en
dc.contributor.author	Koubogiannis, DG	en
dc.contributor.author	Giannakoglou, KC	en
dc.date.accessioned	2014-03-01T01:26:25Z
dc.date.available	2014-03-01T01:26:25Z
dc.date.issued	2007	en
dc.identifier.issn	0271-2091	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/18071
dc.subject	distributed evolutionary algorithms	en
dc.subject	hierarchical optimization	en
dc.subject	metamodels	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Mathematics, Interdisciplinary Applications	en
dc.subject.classification	Mechanics	en
dc.subject.classification	Physics, Fluids & Plasmas	en
dc.subject.other	STRUCTURAL OPTIMIZATION	en
dc.subject.other	GENETIC ALGORITHMS	en
dc.subject.other	NEURAL-NETWORK	en
dc.subject.other	MODELS	en
dc.title	Hierarchical distributed metamodel-assisted evolutionary algorithms in shape optimization	en
heal.type	journalArticle	en
heal.identifier.primary	10.1002/fld.1288	en
heal.identifier.secondary	http://dx.doi.org/10.1002/fld.1288	en
heal.language	English	en
heal.publicationDate	2007	en
heal.abstract	In aerodynamic shape optimization, the availability Of multiple evaluation models of different precision and hence computational cost can be efficiently exploited in a hierarchical evolutionary algorithm. Thus, in this work the demes of a distributed evolutionary algorithm are ordered in levels, with each level employing a different flow analysis method, giving rise to a hierarchical distributed scheme. The arduous task of exploring the design space is undertaken by deities consisting the lower hierarchy level, which use a low-cost flow analysis tool, namely a viscous-inviscid flow interaction method. promising solutions are directed towards the higher level, where these are further evolved based on a high precision/cost evaluation tool, viz. a Navier-Stokes equations solver. The final, optimal solution is obtained front the highest hierarchy level. At each level, metamodels, trained on-line on the outcome of evaluations with the level's analysis tool, are used. The role of metamodels is to allow a parsimonious use of computational resources by filtering the poorly performing individuals in each deme. The entire algorithm has been implemented so as to take advantage of a parallel computing system. The efficiency and effectiveness of the proposed hierarchical distributed evolutionary algorithm have been assessed in the design of a transonic isolated airfoil and a compressor cascade. Remarkable superiority over the conventional evolutionary algorithms has been monitored. Copyright (c) 2006 John Wiley & Sons, Ltd.	en
heal.publisher	JOHN WILEY & SONS LTD	en
heal.journalName	INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS	en
dc.identifier.doi	10.1002/fld.1288	en
dc.identifier.isi	ISI:000243702700006	en
dc.identifier.volume	53	en
dc.identifier.issue	3	en
dc.identifier.spage	455	en
dc.identifier.epage	469	en