Inexact information aided, low-cost, distributed genetic algorithms for aerodynamic shape optimization

Karakasis, MK; Giotis, AP; Giannakoglou, KC

dc.contributor.author	Karakasis, MK	en
dc.contributor.author	Giotis, AP	en
dc.contributor.author	Giannakoglou, KC	en
dc.date.accessioned	2014-03-01T01:19:03Z
dc.date.available	2014-03-01T01:19:03Z
dc.date.issued	2003	en
dc.identifier.issn	0271-2091	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/15345
dc.subject	Distributed evolutionary methods	en
dc.subject	Genetic algorithms	en
dc.subject	Metamodels	en
dc.subject	Optimization	en
dc.subject	Parallelization	en
dc.subject	Radial basis function networks	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	Computational fluid dynamics	en
dc.subject.other	Costs	en
dc.subject.other	Genetic algorithms	en
dc.subject.other	Optimization	en
dc.subject.other	Surrogate evaluation methods	en
dc.subject.other	Aerodynamics	en
dc.subject.other	aerodynamics	en
dc.subject.other	computational fluid dynamics	en
dc.subject.other	genetic algorithm	en
dc.subject.other	optimization	en
dc.subject.other	shape	en
dc.subject.other	turbomachinery	en
dc.title	Inexact information aided, low-cost, distributed genetic algorithms for aerodynamic shape optimization	en
heal.type	journalArticle	en
heal.identifier.primary	10.1002/fld.575	en
heal.identifier.secondary	http://dx.doi.org/10.1002/fld.575	en
heal.language	English	en
heal.publicationDate	2003	en
heal.abstract	Despite its robustness, the design and optimization of aerodynamic shapes using genetic algorithms suffers from high computing cost requirements, due to excessive calls to Computational Fluid Dynamics tools for the evaluation of candidate solutions. To alleviate this problem, either the use of distributed genetic algorithms or the implementation of surrogate evaluation models have separately been proposed in the past. A distributed genetic algorithm relies on the handling of population subsets that evolve in a semi-isolated manner by regularly exchanging their best individuals. It is known that distributed schemes generally outperform single-population ones. On the other band, the implementation of less costly surrogate evaluation tools, such as the autocatalytic radial basis function networks developed by the authors for the purpose of getting rid of most of the 'useless' exact evaluations, reduces considerably the computational cost. The aim of the present paper is to employ a surrogate evaluation model in the context of a distributed genetic algorithm and to demonstrate that the combination of both results in maximum economy in CPU cost. In addition, whenever a multiprocessor system is available, the gain is much more pronounced, since the new optimization method maximizes parallel efficiency. The proposed method is used to solve inverse design and optimization problems in aeronautics and turbomachinery. Copyright (C) 2003 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.575	en
dc.identifier.isi	ISI:000186845000001	en
dc.identifier.volume	43	en
dc.identifier.issue	10-11	en
dc.identifier.spage	1149	en
dc.identifier.epage	1166	en