Large-scale reliability-based structural optimization

Tsompanakis, Y; Papadrakakis, M

dc.contributor.author	Tsompanakis, Y	en
dc.contributor.author	Papadrakakis, M	en
dc.date.accessioned	2014-03-01T01:20:42Z
dc.date.available	2014-03-01T01:20:42Z
dc.date.issued	2004	en
dc.identifier.issn	1615-147X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/16018
dc.subject	Evolution strategies	en
dc.subject	Monte Carlo simulation	en
dc.subject	Parallel computations	en
dc.subject	Preconditioned conjugate gradient	en
dc.subject	Reliability analysis	en
dc.subject	Structural optimization	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Engineering, Multidisciplinary	en
dc.subject.classification	Mechanics	en
dc.subject.other	Evolutionary algorithms	en
dc.subject.other	Gradient methods	en
dc.subject.other	Monte Carlo methods	en
dc.subject.other	Parallel algorithms	en
dc.subject.other	Reliability	en
dc.subject.other	Robustness (control systems)	en
dc.subject.other	Evolution strategies	en
dc.subject.other	Parallel computations	en
dc.subject.other	Preconditioned conjugate gradient	en
dc.subject.other	Reliability analysis	en
dc.subject.other	Structural optimization	en
dc.title	Large-scale reliability-based structural optimization	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/s00158-003-0369-5	en
heal.identifier.secondary	http://dx.doi.org/10.1007/s00158-003-0369-5	en
heal.language	English	en
heal.publicationDate	2004	en
heal.abstract	A robust and efficient methodology is presented for treating large-scale reliability-based structural optimization problems. The optimization is performed with evolution strategies, while the reliability analysis is carried out with the Monte Carlo simulation method incorporating the importance sampling technique to reduce the sample size. Efficient hybrid methods are implemented to solve the reanalysis-type problems that arise in the optimization phase with evolution strategies and in the reliability analysis with Monte Carlo simulations. These hybrid solution methods are based on the preconditioned conjugate gradient algorithm using efficient preconditioning schemes. The numerical tests presented demonstrate the computational advantages of the proposed methods, which become more pronounced for large-scale optimization problems.	en
heal.publisher	SPRINGER-VERLAG	en
heal.journalName	Structural and Multidisciplinary Optimization	en
dc.identifier.doi	10.1007/s00158-003-0369-5	en
dc.identifier.isi	ISI:000220714000005	en
dc.identifier.volume	26	en
dc.identifier.issue	6	en
dc.identifier.spage	429	en
dc.identifier.epage	440	en