On the use of metamodel-assisted, multi-objective evolutionary algorithms

Karakasis, MK; Giannakoglou, KC

dc.contributor.author	Karakasis, MK	en
dc.contributor.author	Giannakoglou, KC	en
dc.date.accessioned	2014-03-01T01:24:48Z
dc.date.available	2014-03-01T01:24:48Z
dc.date.issued	2006	en
dc.identifier.issn	0305-215X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17442
dc.subject	evolutionary algorithms	en
dc.subject	metamodels	en
dc.subject	multi-objective optimization	en
dc.subject	pareto front	en
dc.subject	radial-basis function networks	en
dc.subject	self-organizing maps	en
dc.subject.classification	Engineering, Multidisciplinary	en
dc.subject.classification	Operations Research & Management Science	en
dc.subject.other	OPTIMIZATION	en
dc.subject.other	NETWORKS	en
dc.subject.other	SET	en
dc.title	On the use of metamodel-assisted, multi-objective evolutionary algorithms	en
heal.type	journalArticle	en
heal.identifier.primary	10.1080/03052150600848000	en
heal.identifier.secondary	http://dx.doi.org/10.1080/03052150600848000	en
heal.language	English	en
heal.publicationDate	2006	en
heal.abstract	This article is concerned with the optimal use of metamodels in the context of multi-objective evolutionary algorithms which are based on computationally expensive function evaluations. The goal is to capture Pareto fronts of optimal solutions with the minimum computational cost. In each generation during the evolution, the metamodels act as filters that distinguish the most promising individuals, which will solely undergo exact and costly evaluations. By means of the so-called inexact pre-evaluation phase, based on continuously updated local metamodels, most of the non-promising individuals are put aside without aggravating the overall cost. The gain achieved through this technique is amazing in single-objective problems. However, with more than one objective, noticeable performance degradation occurs. This article scrutinizes the role of metamodels in multi-objective evolutionary algorithms and proposes ways to overcome expected weaknesses and improve their performance. Minimization of mathematical functions as well as aerodynamic shape optimization problems are used for demonstration purposes.	en
heal.publisher	TAYLOR & FRANCIS LTD	en
heal.journalName	ENGINEERING OPTIMIZATION	en
dc.identifier.doi	10.1080/03052150600848000	en
dc.identifier.isi	ISI:000241435800005	en
dc.identifier.volume	38	en
dc.identifier.issue	8	en
dc.identifier.spage	941	en
dc.identifier.epage	957	en