Design of a matrix hydraulic turbine using a metamodel-assisted evolutionary algorithm with PCA-driven evolution operators

Kyriacou, SA; Weissenberger, S; Giannakoglou, KC

dc.contributor.author	Kyriacou, SA	en
dc.contributor.author	Weissenberger, S	en
dc.contributor.author	Giannakoglou, KC	en
dc.date.accessioned	2014-03-01T02:08:36Z
dc.date.available	2014-03-01T02:08:36Z
dc.date.issued	2012	en
dc.identifier.issn	20403607	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/29686
dc.subject	Correlated design variables	en
dc.subject	EAs	en
dc.subject	Evolutionary algorithms	en
dc.subject	Hydraulic turbine design	en
dc.subject	Metamodelling	en
dc.subject	Optimisation	en
dc.title	Design of a matrix hydraulic turbine using a metamodel-assisted evolutionary algorithm with PCA-driven evolution operators	en
heal.type	journalArticle	en
heal.identifier.primary	10.1504/IJMMNO.2012.044713	en
heal.identifier.secondary	http://dx.doi.org/10.1504/IJMMNO.2012.044713	en
heal.publicationDate	2012	en
heal.abstract	To overcome the excessive CPU cost of evolutionary algorithms (EAs) which make use of demanding evaluation models, metamodel-assisted EAs (MAEAs) have been devised and used in either single-objective (SOO) or multi-objective (MOO) problems. MAEAs are based on low-cost surrogate evaluation models that screen out non-promising individuals during the evolution and exclude them from the expensive, problem-specific evaluation. This paper proposes a new technique that further reduces the computational cost of MAEAs. This technique is based on the principal-component-analysis (PCA) of the non-dominated individuals (in MOO) within each generation, to identify dependences among the design variables and, through appropriate rotations, use this piece of information to efficiently 'drive' the application of the evolution operators. The proposed technique is used to perform the multi-operating point design of a matrix hydraulic turbine, where each evaluation is based on a 3D computational fluid dynamics (CFD) code; this is a highly constrained optimisation problem with many objectives, which is herein handled as a two-objective one. Some convincing mathematical function minimisation problems are also worked out using PCA-driven EAs; it is, thus, shown that the PCA-driven evolution operators can be used with or without metamodels. Copyright © 2012 Inderscience Enterprises Ltd.	en
heal.journalName	International Journal of Mathematical Modelling and Numerical Optimisation	en
dc.identifier.doi	10.1504/IJMMNO.2012.044713	en
dc.identifier.volume	3	en
dc.identifier.issue	1-2	en
dc.identifier.spage	45	en
dc.identifier.epage	63	en