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1、NinthMexicanInternationalConferenceonArtificialIntelligenceAcomparationbetweenBeeSwarmOptimizationandGreedyAlgorithmfortheKnapsackProblemwithbeereallocationMarcoAurelioSotelo-Figueroa,Mar´ıadelRosarioBaltazar-Flores,JuanMart´ınCarpio,VictorZamudioDivisiondeEstudiosdePosgradoeInvestigaci´on´In
2、stitutoTecnologicodeLe´on,ITL´Av.TecnologicoS/N,Fracc.JuliandeObreg´on,C.P.37290,Le´onGuanajuato,M´exico.´masotelof@gmail.com,charobalmx@yahoo.com.mx,jmcarpio61@hotmail.com,vic.zamudio@ieee.orgAbstract—TheKnapsackProblemisaclassicalcombinato-oflivingcreaturesmotivatedresearcherstoundertakethe
3、rialproblemwhichcanbesolvedinmanyways.OneofthesestudyofwhattodayisknownasSwarmIntelligence[3].waysistheGreedyAlgorithmwichgivesusanapproximatedTwofundamentalconcepts,self-organizationanddivisionsolutiontotheproblem.Anotherwaytosolveitisusingtheoflabour,arenecessaryandsufficientpropertiestoobta
4、inSwarmIntelligenceapproach,basedonthestudyofactionsofindividualsinvariousdecentralizedsystems.Optimizationswarmintelligentbehavior.algorithmsinspiredontheintelligentbehaviorofhoneybeesareamongthemostrecentlyintroducedpopulationbasedTheBeeAlgorithm[4],orBA,isalsopartofthetechniques.Inthispape
5、r,anovelhybridalgorithmbasedonSwarmIntelligenceandthismimicsthehoneybeesandBeesAlgorithmandParticleSwarmOptimizationisappliedtotheirforagingbehavior.ThisalgorithmisbasedonarandomtheKnapsackProblem,althoughthecombinationofBAandsearchontheneighborhoodforcombinatorialandfunctionalPSOisgivenbyBSO
6、,BeeSwarmOptimization,thisalgorithmoptimization.usesthevelocityvector,thecollectivememoriesofPSOandthesearchbasedontheBA,inthiscaseweintroduceanotherwayTheKnapsackProblemisaclassicalcombinatorialprob-tousethebeealgorithminthePSOusingthebeesreallocation.Theobtainedresultsaremuchbetterwhencompa
7、redtothoselem[5][6]andcanbedescribedasfollows:“ImaginetakingprovidedbytheGreedyAlgorithm.atriptowhichyoucanonlycarryabackpackthat,logically,hasalimitedcapacity.Givenasetofitems,eachwithaKeywords-PSO,BA,BSO,KnapsackProblem,SwarmIntel-ligencewe
