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Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.compstruc.2010.07.004
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dc.titleUniformly sampled genetic algorithm with gradient search for structural identification - Part II: Local search
dc.contributor.authorZhang, Z.
dc.contributor.authorKoh, C.G.
dc.contributor.authorDuan, W.H.
dc.date.accessioned2014-06-17T08:27:12Z
dc.date.available2014-06-17T08:27:12Z
dc.date.issued2010-10
dc.identifier.citationZhang, Z., Koh, C.G., Duan, W.H. (2010-10). Uniformly sampled genetic algorithm with gradient search for structural identification - Part II: Local search. Computers and Structures 88 (19-20) : 1149-1161. ScholarBank@NUS Repository. https://doi.org/10.1016/j.compstruc.2010.07.004
dc.identifier.issn00457949
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/66357
dc.description.abstractThis paper investigates several gradient local search methods as an enhancement to Part I, to further improve the computational efficiency while achieving the same identification accuracy. The present study is significant in several ways. First, this study reveals the characteristics of structural identification from the optimization perspective. Second, the "switch point" from global search to local search is determined with due consideration in convergence speed and avoidance of local optima. Finally, the combined strategy based on Part I and Part II with a particular local search (BFGS) is found to achieve substantial improvement in identification efficiency and accuracy. © 2010 Published by Elsevier Ltd. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.compstruc.2010.07.004
dc.sourceScopus
dc.subjectBFGS method
dc.subjectConjugate gradient
dc.subjectOptimization characteristics
dc.subjectPeak shifting
dc.subjectSimulated annealing
dc.subjectStructural identification
dc.typeArticle
dc.contributor.departmentCIVIL ENGINEERING
dc.description.doi10.1016/j.compstruc.2010.07.004
dc.description.sourcetitleComputers and Structures
dc.description.volume88
dc.description.issue19-20
dc.description.page1149-1161
dc.description.codenCMSTC
dc.identifier.isiut000282081900008
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