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外文翻译--鲁棒优化设计的多目标遗传算法 英文版.pdf

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外文翻译--鲁棒优化设计的多目标遗传算法 英文版.pdf

AMULTIOBJECTIVEGENETICALGORITHMFORROBUSTDESIGNOPTIMIZATIONMIANLIGRADUATERESEARCHASSISTANTDEPARTMENTOFMECHANICALENGINEERINGUNIVERSITYOFMARYLANDCOLLEGEPARK,MD20742TEL13014054919EMAILMLI6UMDEDUSHAPOURAZARMPROFESSORDEPARTMENTOFMECHANICALENGINEERINGUNIVERSITYOFMARYLANDCOLLEGEPARK,MD20742TEL13014055250EMAILAZARMUMDEDUVIKRANTAUTEFACULTYRESEARCHASSISTANTDEPARTMENTOFMECHANICALENGINEERINGUNIVERSITYOFMARYLANDCOLLEGEPARK,MD20742TEL13014058726EMAILVIKRANTUMDEDUABSTRACTREALWORLDMULTIOBJECTIVEENGINEERINGDESIGNOPTIMIZATIONPROBLEMSOFTENHAVEPARAMETERSWITHUNCONTROLLABLEVARIATIONSTHEAIMOFSOLVINGSUCHPROBLEMSISTOOBTAINSOLUTIONSTHATINTERMSOFOBJECTIVESANDFEASIBILITYAREASGOODASPOSSIBLEANDATTHESAMETIMEARELEASTSENSITIVETOTHEPARAMETERVARIATIONSSUCHSOLUTIONSARESAIDTOBEROBUSTOPTIMUMSOLUTIONSINORDERTOINVESTIGATETHETRADEOFFBETWEENTHEPERFORMANCEANDROBUSTNESSOFOPTIMUMSOLUTIONS,WEPRESENTANEWROBUSTMULTIOBJECTIVEGENETICALGORITHMRMOGATHATOPTIMIZESTWOOBJECTIVESAFITNESSVALUEANDAROBUSTNESSINDEXTHEFITNESSVALUESERVESASAMEASUREOFPERFORMANCEOFDESIGNSOLUTIONSWITHRESPECTTOMULTIPLEOBJECTIVESANDFEASIBILITYOFTHEORIGINALOPTIMIZATIONPROBLEMTHEROBUSTNESSINDEX,WHICHISBASEDONANONGRADIENTBASEDPARAMETERSENSITIVITYESTIMATIONAPPROACH,ISAMEASURETHATQUANTITATIVELYEVALUATESTHEROBUSTNESSOFDESIGNSOLUTIONSRMOGADOESNOTREQUIREAPRESUMEDPROBABILITYDISTRIBUTIONOFUNCONTROLLABLEPARAMETERSANDALSODOESNOTUTILIZETHEGRADIENTINFORMATIONOFTHESEPARAMETERSTHREEDISTANCEMETRICSAREUSEDTOOBTAINTHEROBUSTNESSINDEXANDROBUSTSOLUTIONSTOILLUSTRATEITSAPPLICATION,RMOGAISAPPLIEDTOTWOWELLSTUDIEDENGINEERINGDESIGNPROBLEMSFROMTHELITERATURECATEGORIESANDSUBJECTDESCRIPTORSG16OPTIMIZATION–NONLINEARPROGRAMMING;GENERALTERMSDESIGN,ALGORITHMSKEYWORDSMULTIOBJECTIVEGENETICALGORITHMS,ROBUSTDESIGNOPTIMIZATION,PERFORMANCEANDROBUSTNESSTRADEOFF1INTRODUCTIONTHEREAREMANYENGINEERINGOPTIMIZATIONPROBLEMSINTHEREALWORLDTHATHAVEPARAMETERSWITHUNCONTROLLABLEVARIATIONSDUETONOISEORUNCERTAINTYTHESEVARIATIONSCANSIGNIFICANTLYDEGRADETHEPERFORMANCEOFOPTIMUMSOLUTIONSANDCANEVENCHANGETHEFEASIBILITYOFOBTAINEDSOLUTIONSTHEIMPLICATIONSOFSUCHVARIATIONSAREMORESERIOUSINENGINEERINGDESIGNPROBLEMSTHATOFTENHAVEABOUNDEDFEASIBLEDOMAINAND/ORWHERETHEOPTIMUMSOLUTIONSLIEONTHEBOUNDARYOFTHEFEASIBLEDOMAINMANYMETHODSANDAPPROACHESHAVEBEENPROPOSEDINTHELITERATURETOOBTAINROBUSTDESIGNSOLUTIONS;THATIS,FEASIBLEDESIGNALTERNATIVESTHATAREOPTIMUMINTHEIROBJECTIVESANDWHOSEOBJECTIVEPERFORMANCEORFEASIBILITYORBOTHISINSENSITIVETOTHEPARAMETERVARIATIONSGENERALLY,THOSEAPPROACHESCANBECLASSIFIEDINTOTWOTYPESSTOCHASTICAPPROACHESANDDETERMINISTICAPPROACHESSTOCHASTICAPPROACHESUSETHEPROBABILITYINFORMATIONOFTHEVARIABLEPARAMETERS,IE,THEIREXPECTEDVALUEANDVARIANCE,TOMINIMIZETHESENSITIVITYOFSOLUTIONSEG,PARKINSONETAL1,YUANDISHII2,JUNGANDLEE3FOROBJECTIVEROBUSTOPTIMIZATION;ANDDUANDCHEN4,CHENETAL5,TU,CHOIANDPARK6,7,YOUNETAL8ANDRAY9FORFEASIBILITYROBUSTOPTIMIZATION–ALSOCALLEDRELIABILITYOPTIMIZATIONALSO,JINANDSENDHOFF10PROPOSEDANEVOLUTIONARYAPPROACHTODEALWITHTHETRADEOFFBETWEENPERFORMANCEANDROBUSTNESSUSINGVARIANCEINFORMATIONTHEMAINSHORTCOMINGOFSTOCHASTICAPPROACHESISTHATTHEPROBABILITYDISTRIBUTIONFORTHEUNCONTROLLABLEPARAMETERSISKNOWNORPRESUMEDHOWEVER,ITISDIFFICULTOREVENIMPOSSIBLETOOBTAINSUCHINFORMATIONBEFOREHANDINREALWORLDENGINEERINGDESIGNPROBLEMSDETERMINISTICAPPROACHES,ONTHEOTHERHAND,OBTAINROBUSTOPTIMUMDESIGNSOLUTIONSUSINGGRADIENTINFORMATIONOFTHEPARAMETERSEG,BALLINGETAL11,SUNDARESANETAL12,13,ZHUANDTING14,LEEANDPARK15,SUANDRENAUD16,MESSACANDYAHAYA17ORUSINGANONGRADIENTBASEDPARAMETERSENSITIVITYESTIMATIONGUNAWANANDAZARM1821THEAIMOFTHEGUNAWANANDAZARM’SAPPROACH1821ISTOOBTAINOPTIMUMSOLUTIONSWHICHESSENTIALLYSATISFYANADDITIONALROBUSTNESSCONSTRAINTTHATISPRESCRIBEDBYTHEDECISIONMAKERDMINTHISPAPER,WEPRESENTANEWDETERMINISTICAPPROACHTOINVESTIGATETHETRADEOFFBETWEENTHEPERFORMANCEANDROBUSTNESSPERMISSIONTOMAKEDIGITALORHARDCOPIESOFALLORPARTOFTHISWORKFORPERSONALORCLASSROOMUSEISGRANTEDWITHOUTFEEPROVIDEDTHATCOPIESARENOTMADEORDISTRIBUTEDFORPROFITORCOMMERCIALADVANTAGEANDTHATCOPIESBEARTHISNOTICEANDTHEFULLCITATIONONTHEFIRSTPAGETOCOPYOTHERWISE,ORREPUBLISH,TOPOSTONSERVERSORTOREDISTRIBUTETOLISTS,REQUIRESPRIORSPECIFICPERMISSIONAND/ORAFEEGECCO’05,JUNE2529,2005,WASHINGTON,DC,USACOPYRIGHT2005ACM1595930108/05/0006500771OFOPTIMUMSOLUTIONS,BASEDONAMULTIOBJECTIVEGENETICALGORITHMMOGATHEGOALOFOURAPPROACHISTOSIMULTANEOUSLYOPTIMIZEIAMEASUREOFTHEOPTIMUMSOLUTIONS’PERFORMANCE,IE,THEFITNESSVALUE,THATACCOUNTSFOROBJECTIVEANDCONSTRAINTVALUESINTHEORIGINALOPTIMIZATIONPROBLEMDEFINEDINSECTION2,ANDIIAMEASUREOFTHEOPTIMUMSOLUTIONS’ROBUSTNESS,THEROBUSTNESSINDEX,ORIGINALLYPROPOSEDBYGUNAWANANDAZARM1821,EXTENDEDINTHISPAPERWITHTHEUSEOFTWOADDITIONALDISTANCENORMSTHISAPPROACHISADETERMINISTICMETHODUSINGNONGRADIENTBASEDPARAMETERSENSITIVITYESTIMATION,WHICHCANBEAPPLIEDTOOPTIMIZATIONPROBLEMSHAVINGOBJECTIVEAND/ORCONSTRAINTFUNCTIONSTHATARENONDIFFERENTIABLEWITHRESPECTTOTHEPARAMETERSANYMOGAINTHELITERATURECANBEAPPLIEDTOTHISAPPROACHINGUNAWANANDAZARM’SAPPROACH1821,THEAUTHORSTRIEDTOOBTAINOPTIMUMSOLUTIONSTHATAREINSENSITIVETOTHEPARAMETERVARIATIONINOTHERWORDS,THEROBUSTNESSREQUI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