Evolian: Evolutionary Optimization Based on Lagrangian with Constraint Scaling

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dc.contributor.authorMyung, Hyunko
dc.contributor.authorKim, Jong-Hwanko
dc.date.accessioned2009-11-17T05:32:27Z-
dc.date.available2009-11-17T05:32:27Z-
dc.date.created2012-02-06-
dc.date.created2012-02-06-
dc.date.created2012-02-06-
dc.date.issued1997-04-
dc.identifier.citationLecture Notes on Computer Science (LNCS) 1213, v.1213, pp.177 - 188-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10203/12730-
dc.description.abstractIn this paper, an evolutionary optimization method, Evolian, is proposed for the general constrained optimization problem, which incorporates the concept of (1) a multi-phase optimization process and (2) constraint scaling techniques to resolve problem of ill-conditioning. In each phase of Evolian, the typical evolutionary programming (EP) is performed using an augmented Lagrangian objective function with a penalty parameter fixed. If there is no improvement in the best objective function in one phase, another phase of Evolian is performed after scaling the constraints and then updating the Lagrange multipliers and penalty parameter. This procedure is repeated until a satisfactory solution is obtained. Computer simulation results indicate that Evolian gives outperforming or at least reasonable results for multivariable heavily constrained function optimization as compared to other evolutionary computation-based methods.-
dc.languageEnglish-
dc.language.isoen_USen
dc.publisherSpringer-
dc.titleEvolian: Evolutionary Optimization Based on Lagrangian with Constraint Scaling-
dc.typeArticle-
dc.type.rimsART-
dc.citation.volume1213-
dc.citation.beginningpage177-
dc.citation.endingpage188-
dc.citation.publicationnameLecture Notes on Computer Science (LNCS) 1213-
dc.identifier.doi10.1007/BFb0014810-
dc.embargo.liftdate9999-12-31-
dc.embargo.terms9999-12-31-
dc.contributor.localauthorMyung, Hyun-
dc.contributor.localauthorKim, Jong-Hwan-
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