Multi-database exploration of large design spaces in the framework of cascade evolutionary structural sizing optimization

Charmpis, DC; Lagaros, ND; Papadrakakis, M

dc.contributor.author	Charmpis, DC	en
dc.contributor.author	Lagaros, ND	en
dc.contributor.author	Papadrakakis, M	en
dc.date.accessioned	2014-03-01T01:22:48Z
dc.date.available	2014-03-01T01:22:48Z
dc.date.issued	2005	en
dc.identifier.issn	0045-7825	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/16657
dc.subject	Cascade	en
dc.subject	Database	en
dc.subject	Design space	en
dc.subject	Evolutionary algorithms	en
dc.subject	Sizing optimization	en
dc.subject.classification	Engineering, Multidisciplinary	en
dc.subject.classification	Mathematics, Interdisciplinary Applications	en
dc.subject.classification	Mechanics	en
dc.subject.other	Algorithms	en
dc.subject.other	Database systems	en
dc.subject.other	Optimization	en
dc.subject.other	Problem solving	en
dc.subject.other	Structural frames	en
dc.subject.other	Trusses	en
dc.subject.other	Design space	en
dc.subject.other	Optimal designs	en
dc.subject.other	Optimal solutions	en
dc.subject.other	Variables	en
dc.subject.other	Structural analysis	en
dc.subject.other	mathematical method	en
dc.subject.other	optimization	en
dc.subject.other	structural performance	en
dc.title	Multi-database exploration of large design spaces in the framework of cascade evolutionary structural sizing optimization	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.cma.2004.12.020	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.cma.2004.12.020	en
heal.language	English	en
heal.publicationDate	2005	en
heal.abstract	In discrete sizing optimization of truss and frame structures the design variables take values from databases, which are usually populated with a relatively small number of cross-section types and sizes. The aim of this work is to allow the use of large-size databases in discrete structural sizing optimization problems, in order to enrich the set of design variable options and increase the potential of achieving high-quality optimal designs. For this purpose, the concept of coarse database is introduced, according to which smaller-size versions of an appropriately ordered large database can be constructed, This concept is combined with the idea of cascading, which allows a single optimization problem to be tackled with a number of autonomous optimization stages. Under this context, several coarse versions of the same full-size database are formed, in order to utilize a different database in each cascade stage executed with an evolutionary optimization algorithm. The first optimization stages of the resulting multi-database cascade procedure make use of the coarsest database versions available and serve the purpose of basic design space exploration. The last stages exploit finer databases (including the original full-size database) and aim in fine tuning the achieved optimal solution. Based on the reported numerical results, multi-database cascading proves to be an effective tool for the handling of large databases and corresponding extensive design spaces in the framework of discrete structural sizing optimization applications. (c) 2005 Published by Elsevier B.V.	en
heal.publisher	ELSEVIER SCIENCE SA	en
heal.journalName	Computer Methods in Applied Mechanics and Engineering	en
dc.identifier.doi	10.1016/j.cma.2004.12.020	en
dc.identifier.isi	ISI:000230014800006	en
dc.identifier.volume	194	en
dc.identifier.issue	30-33 SPEC. ISS.	en
dc.identifier.spage	3315	en
dc.identifier.epage	3330	en