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改进的粒子群优化算法用于方向图阵列综合.docx

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改进的粒子群优化算法用于方向图阵列综合 Title:ImprovedParticleSwarmOptimizationforDirectionalArraySynthesis Abstract: Thedirectionalarraysynthesisprobleminvolvestheoptimalarrangementandconfigurationofantennaelementsinordertoachievedesiredradiationpatterns.Inrecentyears,ParticleSwarmOptimization(PSO)hasemergedasaneffectiveoptimizationalgorithmforsolvingvariousengineeringproblems.However,thetraditionalPSOalgorithmsuffersfromprematureconvergenceandslowconvergencerates.Inthispaper,weproposeanimprovedPSOalgorithmfordirectionalarraysynthesis,whichaddressestheselimitationsandprovidesbettersolutions. 1.Introduction: Directionalarraysynthesisplaysacrucialroleinwirelesscommunicationsystems,radarsystems,andsatellitecommunication.Thegoalistodesignanantennaarraywithspecificradiationpatterns,suchashighgain,narrowbeamwidth,andlowsidelobes.Theoptimizationprocessinvolvesdeterminingthepositionsandexcitationsofantennaelements. 2.ParticleSwarmOptimization: ParticleSwarmOptimization(PSO)isapopulation-basedglobaloptimizationalgorithminspiredbythecollectivebehaviorofbirdflockingandfishschooling.IntraditionalPSO,eachparticlerepresentsapotentialsolutionandmovesinthesearchspacetowardthebestsolutionfoundsofarbyitselfanditsneighbors.However,PSOsuffersfromprematureconvergenceduetoitslackofdiversityandslowconvergencerates,whichlimitsitseffectivenessforcomplexengineeringproblems. 3.ProposedImprovedPSOAlgorithm: TheimprovedPSOalgorithmproposedinthispaperincorporatesthefollowingenhancements: 3.1InertiaWeightAdaptation:Thisadaptationallowsthesearchprocesstoexploremorediverseregionsofthesearchspaceintheearlystages,whileconvergingtowardsoptimalsolutionsinthelaterstages.Theinertiaweightisdynamicallyupdatedbasedontheconvergencerateandadaptivelyadjustedduringtheoptimizationprocess. 3.2NeighborhoodTopology:Insteadofusingafixedneighborhoodtopology,weintroducedynamicneighborhoodstructuresthatchangeateachiteration.Thispromotesglobalexplorationandavoidsgettingstuckinlocaloptima. 3.3ChaoticInitialization:Chaoticinitializationisut