Publication:
Experiments with New Stochastic Global Optimization Search Techniques

dc.contributor.authorÖZDAMAR, LİNET
dc.contributor.authorDemirhan, Melek
dc.date.accessioned2014-11-05T14:42:45Z
dc.date.available2014-11-05T14:42:45Z
dc.date.issued2000-08
dc.description.abstractIn this paper several probabilistic search techniques are developed for global optimization under three heuristic classifications: simulated annealing, clustering methods and adaptive partitioning algorithms. The algorithms proposed here combine different methods found in the literature and they are compared with well-established approaches in the corresponding areas. Computational results are obtained on 77 small to moderate size (up to 10 variables) nonlinear test functions with simple bounds and Is large size test functions (up to 400 variables) collected from literature.tr_TR
dc.identifier.issn0305-0548
dc.identifier.scopus2-s2.0-0034111264
dc.identifier.urihttp://hdl.handle.net/11413/819
dc.identifier.urihttps://doi.org/10.1016/S0305-0548(99)00054-4
dc.identifier.wos86865700002
dc.language.isoen
dc.publisherPERGAMON-ELSEVIER SCIENCE LTD.
dc.relationCOMPUTERS & OPERATIONS RESEARCHtr_TR
dc.subjectprobabilistic search methodstr_TR
dc.subjectglobal optimizationtr_TR
dc.subjectadaptive partitioning algorithmstr_TR
dc.subjectfuzzy measurestr_TR
dc.subjectolasılıklı arama yöntemleritr_TR
dc.subjectglobal optimizasyontr_TR
dc.subjectadaptif bölümleme algoritmalarıtr_TR
dc.subjectbulanık önlemlertr_TR
dc.titleExperiments with New Stochastic Global Optimization Search Techniquestr_TR
dc.typeArticle
dspace.entity.typePublication
local.indexed.atWOS
local.indexed.atScopus

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