Synergetic use of different evaluation, parameterization and search tools within a multilevel optimization platform

Kampolis, IC; Giannakoglou, KC

dc.contributor.author	Kampolis, IC	en
dc.contributor.author	Giannakoglou, KC	en
dc.date.accessioned	2014-03-01T01:37:11Z
dc.date.available	2014-03-01T01:37:11Z
dc.date.issued	2011	en
dc.identifier.issn	1568-4946	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/21478
dc.subject	Design optimization	en
dc.subject	Evolutionary algorithms	en
dc.subject	Gradient-based optimization	en
dc.subject	Hierarchical search	en
dc.subject	Metamodels	en
dc.subject	Multilevel algorithm	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.other	Design optimization	en
dc.subject.other	Gradient-based optimization	en
dc.subject.other	Hierarchical search	en
dc.subject.other	Meta model	en
dc.subject.other	Multilevel algorithm	en
dc.subject.other	Design	en
dc.subject.other	Multiobjective optimization	en
dc.subject.other	Parameterization	en
dc.subject.other	Shape optimization	en
dc.subject.other	Evolutionary algorithms	en
dc.title	Synergetic use of different evaluation, parameterization and search tools within a multilevel optimization platform	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.asoc.2009.12.024	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.asoc.2009.12.024	en
heal.language	English	en
heal.publicationDate	2011	en
heal.abstract	This paper presents the synergetic use of different evaluation tools, parameterization schemes and search methods on the levels of a multilevel optimization platform to efficiently solve single-and multi-objective computationally demanding optimization problems. The platform is formed by a number of levels which concurrently search for optimal solutions, by regularly exchanging promising individual solutions. Each level is associated with a problem-specific evaluation tool with its own accuracy and computational cost, a parameterization scheme which determines the design variables and their mapping to generate individual solutions and a search algorithm which is either a metamodel-assisted evolutionary algorithm or a gradient-based method. The use of the multilevel platform with only one of the aforementioned features changing from level to level was presented in a previous paper by the authors. The present paper shows that the combined use of hierarchical evaluation, hierarchical parameterization and hierarchical search decreases further the computational cost by increasing the efficiency of the optimization method. This is demonstrated on function minimization and aerodynamic shape optimization problems; though only two levels are used herein, this is not a restriction and the optimization platform may accommodate any number of them. (C) 2009 Elsevier B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE BV	en
heal.journalName	Applied Soft Computing Journal	en
dc.identifier.doi	10.1016/j.asoc.2009.12.024	en
dc.identifier.isi	ISI:000281591300064	en
dc.identifier.volume	11	en
dc.identifier.issue	1	en
dc.identifier.spage	645	en
dc.identifier.epage	651	en