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一种改进的花朵授粉优化算法.docx

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一种改进的花朵授粉优化算法 Title:AnEnhancedFlowerPollinationOptimizationAlgorithm Abstract: Flowerpollinationisanaturalprocesswhereinflowersattractpollinatorstoensuresuccessfulreproduction.Inspiredbythisprocess,theFlowerPollinationOptimizationAlgorithm(FPOA)wasproposedasapopulation-basedmetaheuristicalgorithmtosolveoptimizationproblems.ThispaperintroducesanenhancedversionofFPOA,incorporatingnovelfeaturesandstrategiestoimproveitsefficiencyandconvergencespeed.Theperformanceoftheproposedalgorithmisevaluatedthroughcomparativeexperimentsonasetofbenchmarkproblems,demonstratingitssuperiorityovertheoriginalFPOAandotherstate-of-the-artalgorithms. 1.Introduction Optimizationalgorithmsaimtofindanoptimalsolutionforagivenproblem,mimickingnaturalprocessesorinspiredbybiologicalphenomena.TheFlowerPollinationOptimizationAlgorithm(FPOA)wasintroducedbyYangin[1],inspiredbythepollinationbehaviorofflowers.FPOAemploysapopulation-basedapproach,wherethesolutioncandidatesarerepresentedasflowersinamultidimensionalsearchspace.However,theoriginalFPOAhascertainlimitations,suchasslowconvergenceandsuboptimalsolutions.Inthispaper,anenhancedversionofFPOAisproposedtoaddresstheselimitationsandimproveitsoverallperformance. 2.EnhancedFlowerPollinationOptimizationAlgorithm 2.1.FlowerRepresentation IntheoriginalFPOA,flowersarerepresentedascontinuousvectorsinthesearchspace.ThisrepresentationismodifiedintheenhancedFPOAtoaccommodatebothcontinuousanddiscretevariables.Ahybridrepresentationisemployedwherecontinuousvariablesarerepresentedbyvectors,whilediscretevariablesarerepresentedbyintegervalues.Thismodificationallowsforamoreflexiblerepresentationandbetterexplorationofthesearchspace. 2.2.ReproductiveStrategy TheoriginalFPOAemploysarandompollinationstrategy,whichhasbeenobservedtoleadtoslowconvergenceandprematureconvergenceincertaincases.Toovercomethislimitation,theenhancedFPOAincorporatesacombinationofrandomandguidedpollination.Randompollinationallowsforexploration,whileguidedpollinationusestheknowledgegainedfromthebestflo