改进量子行为粒子群算法智能组卷策略研究-豆柴文库

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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