A multi-objective metamodel-assisted memetic algorithm with strength-based local refinement

Georgopoulou, CA; Giannakoglou, KC

dc.contributor.author	Georgopoulou, CA	en
dc.contributor.author	Giannakoglou, KC	en
dc.date.accessioned	2014-03-01T01:29:34Z
dc.date.available	2014-03-01T01:29:34Z
dc.date.issued	2009	en
dc.identifier.issn	0305-215X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19315
dc.subject	metamodel-assisted memetic algorithms	en
dc.subject	fitness function	en
dc.subject	strength	en
dc.subject	multi-objective optimization	en
dc.subject.classification	Engineering, Multidisciplinary	en
dc.subject.classification	Operations Research & Management Science	en
dc.subject.other	EVOLUTIONARY OPTIMIZATION	en
dc.subject.other	SEARCH	en
dc.subject.other	MODELS	en
dc.title	A multi-objective metamodel-assisted memetic algorithm with strength-based local refinement	en
heal.type	journalArticle	en
heal.identifier.primary	10.1080/03052150902866577	en
heal.identifier.secondary	http://dx.doi.org/10.1080/03052150902866577	en
heal.language	English	en
heal.publicationDate	2009	en
heal.abstract	Metamodel-assisted evolutionary algorithms are low-cost optimization methods for CPU-demanding problems. Memetic algorithms combine global and local search methods, aiming at improving the quality of promising solutions. This article proposes a metamodel-assisted memetic algorithm which combines and extends the capabilities of the aforementioned techniques. Herein, metamodels undertake a dual role: they perform a low-cost pre-evaluation of population members during the global search and the gradient-based refinement of promising solutions. This reduces significantly the number of calls to the evaluation tool and overcomes the need for computing the objective function gradients. In multi-objective problems, the selection of individuals for refinement is based on domination and distance criteria. During refinement, a scalar strength function is maximized and this proves to be beneficial in constrained optimization. The proposed metamodel-assisted memetic algorithm employs principles of Lamarckian learning and is demonstrated on mathematical and engineering applications.	en
heal.publisher	TAYLOR & FRANCIS LTD	en
heal.journalName	ENGINEERING OPTIMIZATION	en
dc.identifier.doi	10.1080/03052150902866577	en
dc.identifier.isi	ISI:000274362400002	en
dc.identifier.volume	41	en
dc.identifier.issue	10	en
dc.identifier.spage	909	en
dc.identifier.epage	923	en