基于改进的遗传蚁群混合算法的tsp问题求解分析

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1、英文摘要AbstractTSPisnotonlyatypicalcombinatorialoptimizationproblem，butalsoaNP—hardproblem，whichiseasy．to．describebutdifficulttodealwith．Foralongtime，peoplehavebeenlookingforanefficient，fastapproximationalgorithmtosolveaLarge。scaleproblemaccuratelyinareasonab

2、letime．Amongthem，typicalofintelligentoptimizationalgorithmshaveAntColonyalgorithmandGeneticalgorithmaswellasmixofbothalgorithmsandSOon．ThispapermainlystudythesolutionofTSPbasedononeImprovementofGeneticandAntColonyalgorithm．Themainresearchworksareasfollows：

3、(1)WiththecharacteristicsofTSPproblem，intheearlyTSPproblemsolvingwithapplicationoftheDelaunaytriangulationofthecandidatesetofstrategiestoreducethesearchspaceofthesolvingofproblems，andfurtheracceleratethesearchingspeed．Andasthebasisforusetheimprovementofalg

4、orithmsolvingTSP．(2)ThatstressonanalysetheshortcomingsofAntcolonyalgorithm，Geneticalgorithmthetraditionalmixofbothalgorithm．Forwhichinsufficient，madeanumberofimprovementstothemixofalgorithm：1)Thatintroducesadynamicintegrationofstrategycanensurethebestinteg

5、rationtimeforthetwoalgorithms；2)ThatintroducesgreypredictionmodeltoestimatethedelimitationofpheromoneinMMAS；3)Theintroductionofcloudassociationrules，toachievetheparametersadaptiveadiustingbythemselvesinthemixofalgorithms．Aftertheseimprovements，madeGenetica

6、lgorithmsandAntColonyalgorithmtoabettermixofdynamic，fullplaytotheirstrengthsmoreeffectivelyusedtosolvetheTSPproblem．Toverifythealgorithmperformance，thispaperusetheTSPLIBexperimentaldatatocompareandanalysetherunningresultsoftheimprovedalgorithm，MMASandtheGe

7、neticandAntColonyalgorithmthatbeforethatimproved．Theresultsshowthattheimprovedalgorithmproposedinthispaperrelativelybetterperformance·KeyWords：TSP；DelaunayTriangulation；GeneticandAntColonyDynamicintegrationalgorithm；GreyPrediciton；CloudAssociation目录目录第1章绪论

8、⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯．11．1研究背景与意义⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯．11．2TSP问题的研究现状⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯．21．3本文主要的研究内容⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯31．4本文组织结构⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯4第2章TSP问题及其描述⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯