预览加载中，请您耐心等待几秒...

求解TSP的改进模拟退火算法研究.docx

预览
1/3
2/3
3/3

在线预览结束，喜欢就下载吧，查找使用更方便

下载提示 文本预览

如果您无法下载资料，请参考说明：

1、部分资料下载需要金币，请确保您的账户上有足够的金币

2、已购买过的文档，再次下载不重复扣费

3、资料包下载后请先用软件解压，在使用对应软件打开

求解TSP的改进模拟退火算法研究 Title:AStudyontheImprovedSimulatedAnnealingAlgorithmforTSPSolver Abstract: TheTravelingSalespersonProblem(TSP)isaclassicproblemincombinatorialoptimizationthatinvolvesfindingtheshortestpossibleroutetovisitasetofcitiesandreturntothestartingcity.DuetoitsNP-hardcomplexity,findinganoptimalsolutionforlargeprobleminstancesremainsachallengingtask.ThispaperaimstoinvestigateandevaluatetheeffectivenessoftheImprovedSimulatedAnnealing(SA)algorithmforsolvingtheTSP. 1.Introduction: TheTSPhassignificantapplicationsinawiderangeofdomainssuchaslogistics,transportation,andtelecommunications.Researchershaveproposednumerousalgorithmstotacklethisproblem,amongwhichtheSimulatedAnnealingalgorithmhasshownpromisingresults.However,theoriginalSAalgorithmhascertainlimitations,includingslowconvergenceanddifficultiesindeterminingsuitableparameters.ThisstudyaimstoaddresstheseissuesbyproposinganimprovedSAalgorithm. 2.SimulatedAnnealing: TheSimulatedAnnealingalgorithmisastochasticlocalsearchmethodinspiredbytheannealingprocessinmetallurgy.Itexploresthesolutionspacebyiterativelysearchingforbettersolutionswhileallowingforoccasionaluphillmoves.TheSAalgorithmusesatemperatureparameterthatcontrolsthesearchprocessandachievesabalancebetweenexplorationandexploitation. 3.ImprovedSimulatedAnnealingforTSP: TheproposedimprovedSAalgorithmincorporatesseveralmodificationstoenhanceitsperformance.Thesemodificationsinclude: -Initialization:Anovelinitializationstrategyisintroduced,whichusesagreedyapproachtoconstructahigh-qualityinitialsolution.Thishelpsthealgorithmtoconvergefaster. -CoolingSchedule:Thecoolingscheduledetermineshowthetemperatureisreducedduringthesearchprocess.Toachieveagoodbalancebetweenexplorationandexploitation,acarefullydesignedcoolingschedule,suchasthegeometriccoolingscheduleorthelogarithmiccoolingschedule,isemployed. -NeighborSolutionGeneration:Theheuristicsusedtogenerateneighborsolutionshaveasignificantimpactonthealgorithm'sperformance.Inthisstudy,weinvestigatetheeffectivenessofvariou