一種基于人工蜂群算法的多目標(biāo)路徑?jīng)Q策方法

一種基于人工蜂群算法的多目標(biāo)路徑?jīng)Q策方法摘要:在多目標(biāo)路徑?jīng)Q策中，尋找最短路徑和最優(yōu)路徑是一個(gè)非常重要的問(wèn)題，這個(gè)問(wèn)題已經(jīng)被廣泛研究。本文提出了一種基于人工蜂群算法的多目標(biāo)路徑?jīng)Q策方法。該方法實(shí)現(xiàn)了由多個(gè)目標(biāo)函數(shù)組成的多目標(biāo)構(gòu)建路徑的優(yōu)化問(wèn)題。最短路徑和最短時(shí)間路徑是最常見(jiàn)的目標(biāo)函數(shù)，此外我們還可以將其他目標(biāo)函數(shù)加入到算法中。實(shí)驗(yàn)結(jié)果表明，與其他算法相比，該算法能夠產(chǎn)生更好的性能表現(xiàn)。關(guān)鍵詞：人工蜂群算法，多目標(biāo)路徑?jīng)Q策，優(yōu)化問(wèn)題，目標(biāo)函數(shù)Abstract:Inmulti-objectivepathdecision,findingtheshortestpathandoptimalpathisaveryimportantproblemthathasbeenwidelyresearched.Inthispaper,amulti-objectivepathdecisionmethodbasedonartificialbeecolonyalgorithmisproposed.Themethodrealizestheoptimizationproblemofconstructingapathcomposedofmultipleobjectivefunctions.Theshortestpathandtheshortesttimepatharethemostcommonobjectivefunctions.Inaddition,wecanaddotherobjectivefunctionstothealgorithm.Theexperimentalresultsshowthatcomparedwithotheralgorithms,thealgorithmcanproducebetterperformance.Keywords:ArtificialBeeColonyAlgorithm,multi-objectivepathdecision,optimizationproblem,objectivefunctionIntroduction:Pathdecisionisacriticalandchallengingtaskinmanyfieldsandareas,includingtransportation,supplychainmanagement,andnetworkoptimization.Findingtheshortestpathandoptimalpathisthemostcommonobjectiveofpathdecision.Multiplecriteriamaycomeintoconsiderationwhilemakingthepathdecision,suchasshortestpath,fastestpath,andthepathwiththeleastcost.Artificialbeecolony(ABC)algorithmisametaheuristicoptimizationalgorithmthatmimicstheintelligentforagingbehaviorofhoneybees.ABCalgorithmhasbeenwidelyinvestigatedandappliedinavarietyofoptimizationproblems,suchasneuralnetworkoptimization,imageprocessing,anddataclustering.TheadvantagesofABCincluderapidconvergencecapability,simplicity,andlowcomputationrequirements.Inthispaper,weproposeamulti-objectivepathdecisionmethodbasedonABCalgorithmthatcanoptimizethepathdecisionproblemwithmultiplecriteria.Methodology:Theproposedmethodinvolvesseveralsteps:1.EncodingthepathdecisionproblemThepathdecisionproblemcanbeencodedasagraph,wherenodesrepresentvariouswaypointsorlocations,andedgesrepresentthepathsorconnectionsbetweenthem.Eachedgehasanassociatedweight,whichcanbethedistance,traveltime,oracombinationofmultiplefactors(e.g.,distance,traveltime,elevationchange,etc.).Theobjectivefunctionrepresentsthepathdecisioncriteria,suchastheshortestpathorthefastestpath.2.InitializationABCalgorithmstartsbyinitializingapopulationofsolutioncandidates(i.e.,initialpathsorroutes).Eachsolutioncandidaterepresentsapathinthegraph.Thepopulationsizecanbedeterminedbytrial-and-errororsettoafixednumber.3.Employed-beephaseInthisphase,eachemployedbeerandomlyselectsaneighboringsolutioncandidateandevaluatesitsobjectivefunctionvalue.Basedontheobjectivefunctionvalue,thebeedecidestoeitherkeepitscurrentsolutionorreplaceitwiththenewsolution.Thisdecisionismadebasedonaprobabilityvaluethatiscalculatedbasedonthecurrentobjectivefunctionvalueandthenewobjectivefunctionvalue.4.Onlooker-beephaseInthisphase,onlookerbeesselectsolutionsfromemployedbeesbasedontheirprobabilityvalues.Theonlookerbeesthenrepeatthesameprocessastheemployedbeestogeneratenewsolutioncandidates.5.Scout-beephaseInthisphase,ifanysolutioncandidatebecomesstagnantorhasnotbeenupdatedforacertainnumberofiterations(thresholdvaluecanbeset),ascoutbeeisspawnedtoexplorenewsolutioncandidatesrandomly.Thishelpspreventthealgorithmfromgettingstuckinlocaloptima.6.SolutionupdatingAfterthescout-beephase,thesolutioncandidateswithbetterobjectivefunctionvaluesareselectedasnewsolutions,andthepopulationisupdated.7.TerminationThealgorithmterminateswhenastoppingcriterionismet,suchasamaximumnumberofiterationsortime.ResultsandDiscussion:Inthispaper,weusetwoobjectivefunctionstooptimizethepathdecisionproblem,thetotaldistance,andtotaltraveltime.WecompareourproposedABCalgorithmwithtwootheralgorithms,theantcolonyoptimization(ACO)algorithmandgeneticalgorithm(GA),onseveralbenchmarkdatasets.TheresultsshowthatourproposedABCalgorithmproducesbetterorcomparableresultscomparedtotheothertwoalgorithmsintermsofboththedistanceandtraveltime.ThisisbecauseABCalgorithmefficientlyexploresthesolutionspaceandcanescapefromlocaloptima.ThealgorithmconvergesfasterandreachesmorediversesolutionsthanACOandGA.Conclusion:Inthispaper,weproposedamulti-objectivepathdecisionmethodbasedonABCalgorithm.Byencodingthepathdecisionproblemasagraphandusingmultipleobjectivefunctions,wecanefficientlysearchforthebestpathwithmultiplecriteria.Th