蚁群优化算法英文课件.ppt

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1、蚁群优化算法AntColonyOptimization1142227072桑金嵩2AlgorithmintroductionSwarmintelligenceAlgorithmSwarmintelligenceisakindofintelligentbehaviorthatfromsimpleintelligentindividualsthroughsomeformofaggregationofcollaborative.Itisabionicrandomsearchalgorithmwhichisproposedby

2、imitatingthebiologicalevolutionmechanismandgroupcooperationbehavior.去ArtificialbeecolonyalgorithmBacterialforagingalgorithmFireflyalgorithmParticleSwarmOptimizationArtificialfishswarmalgorithmBirdswarmalgorithm3AlgorithmPropositionGambardellaMacroDorigo4Algorith

3、mDevelopmentSince2001In1996-2001ItalyscholarsDorigo1991Dorigo1991年各种改进算法的提出，应用领域更广引起学者关注，在应用领域得到拓宽ACO首次被系统的提出自然界中真实蚁群集体行为Inspiration5AlgorithmPrincipleHowtofindtheshortestpath?Analogy：ForagingprocessofEscherichiacoliinthehumangutPheromone：Itisclearlythatthemorep

4、heromonethemoreantshavepassed，somoreandmoreantswillcometogether.Positivefeedbackphenomenon：Themoreantswalkthroughapath,thegreaterprobabilityofchoosingthepathinthenexttime.6AlgorithmPrincipleTheintelligentcharacteristicsofnaturalants7AlgorithmPrincipleThemodelofa

5、rtificialant8AlgorithmPrincipleNaturalandartificialantcolonySimilarity：Bothpreferthepaththatpheromoneconcentrationishigh.Difference：（1）Artificialanthascertainabilityofmemory,theycanmemorythenodethathasbeenvisited.（2）Artificialantdoesnotchoosethepathinablindway,t

6、heyconsciouslyfindtheshortestpathincertainrules.Forexample,theycanknowthedistancetonextdestinationinadvanceasforTSPproblem.9AntColonyalgorithmofsolvingcombinatorialoptimizationproblems10ACO(AS)Antk(k=1,2，…,m)choosenextcityaccordingthepheromoneconcentrationinthec

7、onnectedpathamongeachcities,belowcalculationformulashowstheprobabilitythatantKgoesfromIcitytoJcityatT:pheromoneupdatingformula：11ACO(AS)Aimingattheproblemthatantreleasespheromone,M.Dorigohadgiven3differentmodels,theseareant-cycle、ant-quantity、ant-density.Theform

8、ulasareasfollow:1.Ant-cycle2.Ant-quantity3.Ant-densityQisaconstantinaboveformula,expressingthetotalpheromonethatantcirculateonetime;Listhelengththatantkhaspassed;Dist