Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/44868
DC FieldValueLanguage
dc.contributor.authorChen, T.Y.en_US
dc.contributor.author陳定宇zh_TW
dc.contributor.authorCheng, Y.L.en_US
dc.date2010zh_TW
dc.date.accessioned2014-06-06T08:13:58Z-
dc.date.available2014-06-06T08:13:58Z-
dc.identifier.issn0305-215Xzh_TW
dc.identifier.urihttp://hdl.handle.net/11455/44868-
dc.description.abstractThe use of evolutionary algorithms for global optimization has increased rapidly during the past several years. But evolutionary computations have a common drawback: they need a huge number of function evaluations. This makes them inadequate for structural optimization. To overcome this difficulty, the authors propose a method that integrates the evolutionary algorithm with data mining and approximate analysis to find the optimal solution in structural optimization. The approximate analysis is used to replace exact finite element analyses and the data mining is employed to identify feasible solutions. These combined efforts can reduce the computational time and search the feasible region intensively. As a result, the efficiency and quality of structural optimization using evolutionary algorithms will be increased. Some test problems show that the proposed method not only finds the global solution but is also less computationally demanding.en_US
dc.language.isoen_USzh_TW
dc.relationEngineering Optimizationen_US
dc.relation.ispartofseriesEngineering Optimization, Volume 42, Issue 3, Page(s) 205-222.en_US
dc.relation.urihttp://dx.doi.org/10.1080/03052150903110942en_US
dc.subjectstructural optimizationen_US
dc.subjectdata miningen_US
dc.subjectevolution strategyen_US
dc.subjectartificialen_US
dc.subjectneural networken_US
dc.subjectglobal optimizationen_US
dc.subjectmultimodal functionsen_US
dc.subjectgenetic algorithmsen_US
dc.subjectminimumen_US
dc.subjectsearchen_US
dc.subjectdesignen_US
dc.titleData-mining assisted structural optimization using the evolutionary algorithm and neural networken_US
dc.typeJournal Articlezh_TW
dc.identifier.doi10.1080/03052150903110942zh_TW
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