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dc.contributor.authorIyer, Manjula A.en_US
dc.contributor.authorPhillips, Rhonda D.en_US
dc.contributor.authorTrosset, Michael W.en_US
dc.contributor.authorWatson, Layne T.en_US
dc.date.accessioned2013-06-19T14:36:52Z
dc.date.available2013-06-19T14:36:52Z
dc.date.issued2008-05-01
dc.identifierhttp://eprints.cs.vt.edu/archive/00001033/en_US
dc.identifier.urihttp://hdl.handle.net/10919/19429
dc.description.abstractRobust design optimization (RDO) uses statistical decision theory and optimization techniques to optimize a design over a range of uncertainty (introduced by the manufacturing process and unintended uses). Since engineering ob jective functions tend to be costly to evaluate and prohibitively expensive to integrate (required within RDO), surrogates are introduced to allow the use of traditional optimization methods to find solutions. This paper explores the suitability of radically different (deterministic and stochastic) optimization methods to solve prototypical robust design problems. The algorithms include a genetic algorithm using a penalty function formulation, the simultaneous perturbation stochastic approximation (SPSA) method, and two gradient-based constrained nonlinear optimizers (method of feasible directions and sequential quadratic programming). The results show that the fully deterministic standard optimization algorithms are consistently more accurate, consistently more likely to terminate at feasible points, and consistently considerably less expensive than the fully nondeterministic algorithms.en_US
dc.format.mimetypeapplication/pdfen_US
dc.publisherDepartment of Computer Science, Virginia Polytechnic Institute & State Universityen_US
dc.relation.ispartofComputer Science Technical Reportsen_US
dc.subjectAlgorithmsen_US
dc.subjectData structuresen_US
dc.titleNote on the Effectiveness OF Stochastic Optimization Algorithms for Robust Designen_US
dc.typeTechnical reporten_US
dc.identifier.trnumberTR-08-11en_US
dc.type.dcmitypeTexten_US
dc.identifier.sourceurlhttp://eprints.cs.vt.edu/archive/00001033/01/rdoIJPAM08.pdf


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