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1、AbstractBilevelprogrammingproblemsareaclassofhierarchicaloptimizationproblems,whichareusedinmanyareasextensively,suchaseconomyandmanagementandmilitary,etc.Theproblemischaracterizedbythefactthattheconstraintregionofoneoptimizationproblemisdeterminedbytheoptimalityofanother,andisstro
2、ngly-NP.Foramulti-objectiveoptimizationproblem,thereismorethanoneobjectiveintheobjectivefunctionandtheseobjectivesusuallycontradictwitheachother.Bilevelmulti-objectiveoptimizationinvolvesatleastonemulti-objectivefunctionintheleader’sandthefollower’sobjectives,withbilevelhierarchica
3、lstructureaswellasfeaturesofmulti-objectiveoptimization,makingtheproblemhaveimportanttheoreticandpracticevalues.However,ontheotherhand,thefeaturemakestheproblemhardertosolve.Atpresent,thereexistsometransformingapproaches,suchasK-K-Tconditions,theyconvertbilevelmodelintoasingle-leve
4、lcase,andusesingle-levelmulti-objectiveoptimizationmethodstodealwithtransformedone.Theshortcomingisthattheefficiencyofalgorithmsisalwaysdecreasedduetoaddingtoomanyvariables.Inthisthesis,twoclassesofbilevelmulti-objectiveprogrammingproblemsarediscussedandtheevolutionaryalgorithmsare
5、designedbasedonthefeatureoftheproblems.1.Forbilevelprogrammingproblemswithmulti-objectivefunctionsimplyinleader’slevel,followingtheassumptionthatthefollowerhasauniquesolutionforeachfixedleader’svariablevalue,anevolutionaryalgorithmisproposedbasedonNSGA-IIandtheinterpolationtechniqu
6、e.Firstly,somesamplepointsareselectedandthefollower’ssolutionfunctionsareapproximatedbycorrespondinginterpolationfunctions;Secondly,NSGA-IIisusedtoevolvetheleader’svariable,andforeachleader’svariabletheoptimalsolutionofthefollowercanbeobtainedusinginterpolationfunctions.Inordertoob
7、tainbetterapproximationofthefollower’ssolutions,ateachiterationasmallnumberofbetterindividualsareselectedandmodified.Themodifiedpointsaretakenasnewsamplepointstoimprovetheinterpolationfunctions.Finally,inordertodecreasetheamountofcomputation,amulti-criterionevolvingprocessisgiven,w
8、hichmakesthemodificationof
