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改进量子行为粒子群算法智能组卷策略研究.docx

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改进量子行为粒子群算法智能组卷策略研究 Title:ResearchonImprovedQuantum-InspiredParticleSwarmOptimizationforIntelligentTestAssemblyStrategy Abstract: Inrecentyears,thedevelopmentofcomputer-basedintelligenttestassemblyhasgainedsignificantattentioninthefieldofeducation.Thispaperaimstoproposeanimprovedquantum-inspiredparticleswarmoptimization(QPSO)algorithmforefficientlysolvingtheintelligenttestassemblyproblem.Theresearchfocusesonenhancingtheconvergencerateandsearchcapabilityofthetraditionalparticleswarmoptimization(PSO)algorithmthroughtheintegrationofquantumbehaviorandprinciples.TheexperimentalresultsdemonstratetheeffectivenessandsuperiorityoftheproposedQPSOalgorithmincomparisontotraditionalPSOalgorithms.Thisstudyprovidesvaluableinsightsfordevelopingintelligenttestassemblystrategiesineducationalassessmentsystems. 1.Introduction Intelligenttestassemblyisacrucialtaskineducationalassessment,whichinvolvesselectingappropriateitemstoformatesttoevaluatetheknowledgeandabilitiesofstudents.Withtheincreasingdemandforpersonalizededucation,thetraditionalmanualtestassemblyapproachhasbecomeinefficientandtime-consuming.Hence,thereisaneedforautomatedapproachestoimprovethequality,fairness,andefficiencyoftestassembly. 2.LiteratureReview Thissectionprovidesanoverviewofexistingapproachestointelligenttestassembly.Variousmethods,suchasgeneticalgorithms(GA),simulatedannealing(SA),andPSO,havebeenappliedtosolvetheproblem.However,traditionalPSOalgorithmsfacechallengesinachievingoptimalsolutionsduetotheirlimitedexplorationandexploitationabilities. 3.Quantum-InspiredParticleSwarmOptimization ThissectionintroducestheprinciplesofquantummechanicsthatprovidethefoundationfortheQPSOalgorithm.Thebasicconceptsofsuperposition,entanglement,andquantumgatesareexplained,alongwithhowtheyareincorporatedintothePSOalgorithm.Theadaptationofparticlepositionsandvelocitiesbasedonquantumprinciplesenhancesthesearchcapabilityandconvergencerateofthealgorithm. 4.ImprovementStrategies ThissectionproposesseveralimprovementstrategiesfortheQPSOa