Multi-objective design optimization using cascade evolutionary computations

Lagaros, ND; Plevris, V; Papadrakakis, M

dc.contributor.author	Lagaros, ND	en
dc.contributor.author	Plevris, V	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/123456789/16660
dc.subject	Cascade evolutionary algorithms	en
dc.subject	Latin hypercube	en
dc.subject	Multi-objective optimization	en
dc.subject	Robust design 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	Computational methods	en
dc.subject.other	Computer simulation	en
dc.subject.other	Constraint theory	en
dc.subject.other	Curve fitting	en
dc.subject.other	Failure analysis	en
dc.subject.other	Monte Carlo methods	en
dc.subject.other	Optimization	en
dc.subject.other	Pareto principle	en
dc.subject.other	Probability	en
dc.subject.other	Computational efficiency	en
dc.subject.other	Design codes	en
dc.subject.other	Multi-objective optimization	en
dc.subject.other	Optimum design	en
dc.subject.other	Structural analysis	en
dc.subject.other	mathematical method	en
dc.subject.other	optimization	en
dc.title	Multi-objective design optimization using cascade evolutionary computations	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.cma.2004.12.029	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.cma.2004.12.029	en
heal.language	English	en
heal.publicationDate	2005	en
heal.abstract	The consideration of uncertainties in conjunction with the probability of violation of the constraints imposed by the design codes is examined in the framework of structural optimization. The optimum design achieved based on a deterministic formulation is compared, in terms of the optimum weight. the probability of violation of the constraints and the probability of failure, with the optimum designs achieved through a robust design formulation where the variance of the response is considered as an additional criterion, The stochastic finite element problem is solved using the Monte Carlo Simulation method, combined with the Latin Hypercube Sampling technique for improving its computational efficiency. A non-dominant cascade evolutionary algorithm-based methodology is, adopted for the solution of the multi-objective optimization problem encountered, in order to obtain the global Parelo, front curve. (c) 2005 Elsevier B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE SA	en
heal.journalName	Computer Methods in Applied Mechanics and Engineering	en
dc.identifier.doi	10.1016/j.cma.2004.12.029	en
dc.identifier.isi	ISI:000230014800015	en
dc.identifier.volume	194	en
dc.identifier.issue	30-33 SPEC. ISS.	en
dc.identifier.spage	3496	en
dc.identifier.epage	3515	en