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基于量子遗传算法的云服务选择.docx

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基于量子遗传算法的云服务选择 Abstract Inrecentyears,cloudcomputinghasbecomeincreasinglypopularduetoitscost-effectiveandflexiblenature.However,selectingtherightcloudserviceproviderisachallengingtaskduetothevastnumberofprovidersandservicesavailable.Thispaperproposesanovelapproachforcloudserviceproviderselectionbasedonquantum-inspiredgeneticalgorithms.Theproposedapproachemploysgeneticoperatorssuchasmutation,crossover,andselection,andquantum-inspiredoperatorslikequantumgate,superposition,andentanglement.Experimentalresultsdemonstratethattheproposedapproachoutperformsexistinggeneticalgorithm-basedapproachesforcloudserviceproviderselection. Introduction Withtheincreasingpopularityofcloudcomputing,selectingtherightcloudserviceproviderhasbecomeanessentialtaskfororganizationsthatwanttoleveragethebenefitsofcloudcomputingwhilekeepingcostslowandensuringdatasecurity.Theselectionprocessinvolvesevaluatingseveralfactors,suchasservicequality,cost,reliability,security,andavailability,amongothers.Ingeneral,therearetwomainapproachestoselectingacloudserviceprovider:manualselectionandautomatedselection.Manualselectioninvolvestheevaluationofcloudprovidersbasedontheirfeaturesandcapabilities,whichisatime-consumingprocess.Ontheotherhand,automatedselectionemploysmachinelearningalgorithmsandoptimizationtechniquestoselectthebestcloudserviceproviderthatmeetstheuser’srequirements. Recently,evolutionaryalgorithmssuchasgeneticalgorithms(GA)havegainedattentionasapromisingtechniqueforcloudserviceselection.GAisametaheuristicoptimizationtechniqueinspiredbytheconceptofnaturalselectionandgenetics.Ithasbeenwidelyusedinvariousapplicationareas,includingoptimization,scheduling,andmachinelearning.However,GAhasseverallimitationssuchasprematureconvergence,localoptima,andslowconvergencerate.TheselimitationscanreducetheefficiencyandeffectivenessofGAforcloudserviceselection. ToovercomethelimitationsofGA,thispaperproposesanewapproachforcloudserviceproviderselectionbasedonaquantum-inspiredgeneticalgorithm(QGA).QGAisanextensionofG