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1、TransactionsofTianjinUniversityVol.4No.2Nov.1998MULTI2OBJECTIVEOPTIMIZATIONUSINGGENETICALGORITHMWITHLOCALSEARCH3DAIXiaohui33LIMinqiangKOUJisong(SchoolofManagement,TianjinUniversity)AbstractInthispaper,weproposeahybridalgorithmforfindingasetofnon2dominatedsolutionsofamulti
2、2objectiveoptimizationproblem.Intheproposedalgorithm,alocalsearchprocedureisappliedtoeachsolutiongeneratedbygeneticoperations.Theaimoftheproposedalgorithmisnottodetermineasinglefinalsolutionbuttotrytofindallthenon2dominatedsolutionsofamulti2objectiveoptimizationproblem.Th
3、echoiceofthefinalsolutionislefttothedecisionmaker’spreference.Highsearchabilityoftheproposedalgorithmisdemonstratedbycomputersimulation.Keywordsmulti2objective,geneticalgorithm,Paretoset,localsearchΞ3)Thenumberofneighborhoodsolutionsexam2inedforeachmoveinthelocalsearchisr
4、estrictedtopreventthelocalsearchfromspendingalmostallthecomputationtime.4)Atentativesetofnon2dominatedsolutionsisstoredandupdatedinouralgorithm.Thatsetisstoredseparatelyfromacurrentpopulation.5)Tomaintainthediversityofthepopulation,eli2tiststrategywasemployedinouralgorith
5、m.Mostpracticalproblemsrequirethesimultaneousoptimizationofmultiple,oftencompeting,objectives.Inapplicationsofoptimizationtechniques,thesolutiontosuchproblemsisusuallycomputedbycombiningtheobjectivesintoasingleoneaccordingtosomeutili2tyfunction.Inmanycases,however,theutil
6、ityfunc2tionisnotwellknownpriortotheoptimizationpro2cess,andtherefore,thewholeproblemshouldthenbetreatedasamulti2objectiveoptimizationproblem(MOP)withnon2commensurableobjectives.IntheMOP,itisrequiredtofindallpossibletradeoffsamongmultipleobjectivefunctionsthatareusuallyco
7、nflict2ing.Sinceitisdifficulttochooseasinglesolutionforamulti2objectiveoptimizationproblemwithoutiterativeinteractionwiththedecisionmaker,onegeneralap2proachistoshowthatsetofParetooptimalsolutionstothedecisionmaker1.ThenoneoftheParetoopti2malsolutionscanbechosendependingo
8、ntheprefer2ence.TofindalltheParetooptimalsolutions,amulti2objectivegeneticalgorithm(MOGA)withloc
