简介：AnExtendedParticleSwarmOptimizer(EPSO)isproposedinthispaper.Inthisnewalgorithm,notonlythelocalbutalsotheglobalbestpositionwillimpacttheparticle'svelocityupdatingprocess.EPSOisanintegrationofLocalBestparadigm(LBEST)andGlobalBestparadigm(GBEST)anditsignificantlyenhancestheperformanceoftheconventionalparticleswarmoptimizers.TheexperimentresultshaveprovedthatEPSOdeservestobeinvestigated.

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简介：这篇论文主要被奉献给在变化环境与固定拓扑学群的一个班结队。第一，为每个代理人的控制器被采用在代理人的状态和它的邻居的平均状态之间的错误术语建议。第二，为群的一个足够的条件在环境的坡度被围住，起始的位置图被连接的假设下面被介绍完成结队。第三，因为环境是一架飞机，尽管不一个代理人知道这个组的中心在哪儿，每个代理人的速度最后收敛到群中心的速度，这进一步被证明。最后，数字例子被包括说明获得的结果。

简介：Theobjectiveofsteganographyistohidemessagesecurelyincoverobjectsforsecretcommunication.Howtodesignasecuresteganographicalgorithmisstillmajorchallengeinthisre-searchfield.Inthisletter,developingsecuresteganographyisformulatedassolvingaconstrainedIP(IntegerProgramming)problem,whichtakestherelativeentropyofcoverandstegodistributionsastheobjectivefunction.Furthermore,anovelmethodisintroducedbasedonBPSO(BinaryParticleSwarmOptimization)forachievingtheoptimalsolutionofthisprogrammingproblem.Experimentalresultsshowthattheproposedmethodcanachieveexcellentperformanceonpreservingneighboringco-occurrencefeaturesforJPEGsteganography.

简介：Swarm的模拟程序包括四类对象：ModelSwarm，ObserverSwarm，模拟主体和环境。其中，ModelSwarm和ObserverSwarm是swarm类的子类，swarm类是Swarm模拟的基本构造块，一个swarm是一系列对象以及这些对象的行为时间表的组合。主体是一个在其状态和行为规则的基础上能够与其他代表相互作用的自主实体，而对象是一个包含变量的数据结构，这些变量能够记录对象的状态与功能。

简介：ＡｐｐｌｉｃａｔｉｏｎｏｆＰＰｃｌｕｓｔｅｒｍｅｔｈｏｄｉｎｔｈｅｅａｒｔｈｑｕａｋｅｓｗａｒｍａｎａｌｙｓｉｓＳｈｉ－ＹｏｎｇＺＨＯＵ；（周仕勇）Ｌｉｎｇ－ＲｅｎＺＨＵ；（朱令人）ａｎｄＣｈｕａｎ－ＬｉｎｇＤＥＮＧ（邓传玲）（Ｓｅｉｓｍｏｌｏｇｉｃ...

简介：Particleswarmoptimizer(PSO),anewevolutionarycomputationalgorithm,exhibitsgoodperformanceforoptimizationproblems,althoughPSOcannotguaranteeconvergenceofaglobalminimum,evenalocalminimum.However,therearesomeadjustableparametersandrestrictiveconditionswhichcanaffectperformanceofthealgorithm.Inthispaper,thealgorithmareanalyzedasatime-varyingdynamicsystem,andthesufficientconditionsforasymptoticstabilityofaccelerationfactors,incrementofaccelerationfactorsandinertiaweightarededuced.Thevalueoftheinertiaweightisenhancedto(fi1,1).Basedonthededucedprincipleofaccelerationfactors,anewadaptivePSOalgorithm-harmoniousPSO(HPSO)isproposed.FurthermoreitisprovedthatHPSOisaglobalsearchalgorithm.Intheexperiments,HPSOareusedtothemodelidentificationofalinearmotordrivingservosystem.AnAkaikeinformationcriteriabasedfitnessfunctionisdesignedandthealgorithmscannotonlyestimatetheparameters,butalsodeterminetheorderofthemodelsimultaneously.TheresultsdemonstratetheeffectivenessofHPSO.

简介：复杂地球物理的数据的倒置总是解决多参数，非线性、多模式的优化问题。寻找最佳的倒置答案类似于当寻找食物时，在象鸟和蚂蚁那样的群观察的社会行为。在这篇文章，首先，粒子群优化算法详细被描述，并且蚂蚁殖民地算法改善了。然后，方法被用于地球物理的倒置问题的三种不同类型：(1)对噪音敏感的一个线性问题，(2)线性、非线性的问题的同步倒置，并且(3)一个非线性的问题。结果验证他们的可行性和效率。与常规基因算法相比并且退火模仿，他们有更高的集中速度和精确性的优点。与伪相比--牛顿方法和Levenberg-Marquardt方法，他们与克服局部地最佳的答案的能力更好工作。

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简介：TherearesomeadjustableparameterswhichdirecdyinfluencetheperformanceandstabilityofParticleSwarmOp-ttimizationalgorithm.Inthispaper,stabilitiesofPSOwithconstantparametersandtime-varyingparametersareanalyzedwithoutLipschitzconstraint.Necessaryandsufficientstabilityconditionsforaccelerationfactorψandinertiaweightwarepresented.Exper-imentsonbenchmarkfunctionsshowthegoodperfomanceofPSOsatisfyingthestabilitycondition,evenwithoutLipschitzcon-straint.Andtheinertiaweightwvalueisenhancedto(-1,1).

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简介：Acceleratingtheconvergencespeedandavoidingthelocaloptimalsolutionaretwomaingoalsofparticleswarmoptimization(PSO).TheverybasicPSOmodelandsomevariantsofPSOdonotconsidertheenhancementoftheexplorativecapabilityofeachparticle.Thusthesemethodshaveaslowconvergencespeedandmaytrapintoalocaloptimalsolution.Toenhancetheexplorativecapabilityofparticles,aschemecalledexplorativecapabilityenhancementinPSO(ECE-PSO)isproposedbyintroducingsomevirtualparticlesinrandomdirectionswithrandomamplitude.Thelinearlydecreasingmethodrelatedtothemaximumiterationandthenonlinearlydecreasingmethodrelatedtothefitnessvalueofthegloballybestparticleareemployedtoproducevirtualparticles.TheabovetwomethodsarethoroughlycomparedwithfourrepresentativeadvancedPSOvariantsoneightunimodalandmultimodalbenchmarkproblems.ExperimentalresultsindicatethattheconvergencespeedandsolutionqualityofECE-PSOoutperformthestate-of-the-artPSOvariants.

简介：Inordertodesignacomplexlaserresonatorwithmulti-parameters,themethodofparticleswarmoptimization(PSO)algorithmisemployed.Theparametersinfluencingtheresonatorstabilityandmodesizedistributionaretakenintoconsideration,andthestabilitycriteriaindexandthemodesizedistributionareusedastargetvalues.TheabsolutevaluesofthedifferencesbetweenpracticalandthetargetvaluesaresetasthefitnessfunctionforthePSO.Byminimizingthefitnessfunction,alaserresonatorwiththeoptimizedcavityparameterscanbefound.TheanalysesforthedesignexampledemonstratethefeasibilityandvalidityofthePSOmethodinthecomputeraideddesignofmul-ti-parameterslaserresonator.ApplyingPSOalgorithmintheintelligentdesignofsolidstatelaserresonatorscanrealizethe.transitionfrommanualtrial-and-errortocomputerintelligentdesignofthelaserresonators.

简介：Energyconsumptionofsensornodesisoneofthecrucialissuesinprolongingthelifetimeofwirelesssensornetworks.Oneofthemethodsthatcanimprovetheutilizationofsensornodesbatteriesistheclusteringmethod.Inthispaper,weproposeagreenclusteringprotocolformobilesensornetworksusingparticleswarmoptimization(PSO)algorithm.Wedefineanewfitnessfunctionthatcanoptimizetheenergyconsumptionofthewholenetworkandminimizetherelativedistancebetweenclusterheadsandtheirrespectivemembernodes.Wealsotakeintoaccountthemobilityfactorwhendefiningtheclustermembership,sothatthesensornodescanjointheclusterthathasthesimilarmobilitypattern.Theperformanceoftheproposedprotocoliscomparedwithwell-knownclusteringprotocolsdevelopedforwirelesssensornetworkssuchasLEACH(low-energyadaptiveclusteringhierarchy)andprotocolsdesignedforsensornetworkswithmobilenodescalledCM-IR(clusteringmobility-invalidround).Inaddition,wealsomodifytheimprovedversionofLEACHcalledMLEACH-C,sothatitisapplicabletothemobilesensornodesenvironment.SimulationresultsdemonstratethattheproposedprotocolusingPSOalgorithmcanimprovetheenergyconsumptionofthenetwork,achievebetternetworklifetime,andincreasethedatadeliveredatthebasestation.

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简介：由于他们的结构上的二金属的nanoparticles(NP)的化学、物理的性质的依赖，他们的结构的特征的基本理解为他们的综合体和宽应用是关键的。在这篇文章，Au-Pd二金属的NP的系统的原子水平的调查被在不同Au/Pd比率和不同尺寸与量修正Sutton陈潜力(Q-SC)使用改进粒子群优化(IPSO)进行。在IPSO，模仿的退火被介绍进古典粒子群优化(PSO)改进有效性和可靠性。另外，结构的稳定性和结构的特征上的起始的结构，粒子尺寸和作文的影响也被学习。模拟结果表明起始的结构在稳定的结构上有小效果，但是极大地影响收敛的率，并且起始的结构清楚地是的混合的集中率快核心壳和阶段比那些组织。我们发现Au-PdNP比较喜欢结构与在外部层Au富有当时在内部的Pd富有。特别，当Au/Pd比率是6:4时，nanoparticle(NP)的结构介绍标准化Pd核心Au壳结构。

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简介：Awayofresolvingspreadingcodemismatchesinblindmultiuserdetectionwithaparticleswarmoptimization(PSO)approachisproposed.IthasbeenshownthatthePSOalgorithmincorporatingthelinearsystemofthedecorrelatingdetector,whichistermedasdecorrelatingPSO(DPSO),cansignificantlyimprovethebiterrorrate(BER)andthesystemcapacity.Asthecodemismatchoccurs,theoutputBERperformanceisvulnerabletodegradationforDPSO.Withablinddecorrelatingscheme,theproposedblindDPSO(BDPSO)offersmorerobustcapabilitiesoverexistingDPSOundercodemismatchscenarios.

简介：Aimingtoreducethecomputationalcostsandconvergetoglobaloptimum,anovelmethodisproposedtosolvetheoptimizationofacostfunctionintheestimationofdirectionofarrival(DOA).Inthismethod,ageneticalgorithm(GA)andfuzzydiscreteparticleswarmoptimization(FDPSO)areappliedtooptimizethedirectionofarrivalandpowerparametersofthemodesimultaneously.Firstly,theGAalgorithmisappliedtomakethesolutionfallintotheglobalsearching.Secondly,theFDPSOmethodisutilizedtonarrowdownthesearchfield.InFDPSO,achaoticfactorandacrossovermethodareaddedtospeeduptheconvergence.Thisapproachhasbeendemonstratedthroughsomecomputationalsimulations.ItisshownthattheproposedalgorithmcanestimateboththeDOAandthepowersaccurately.Itismoreefficientthansomepresentmethods,suchastheNewton-likealgorithm,Akaikeinformationcritical(AIC),particleswarmoptimization(PSO),andgeneticalgorithmwithparticleswarmoptimization(GA-PSO).

简介：OptimalformationreconfigurationcontrolofmultipleUninhabitedCombatAirVehicles(UCAVs)isacomplicatedglobaloptimumproblem.ParticleSwarmOptimization(PSO)isapopulationbasedstochasticoptimizationtechniqueinspiredbysocialbehaviourofbirdflockingorfishschooling.PSOcanachievebetterresultsinafaster,cheaperwaycomparedwithotherbio-inspiredcomputationalmethods,andtherearefewparameterstoadjustinPSO.Inthispaper,weproposeanimprovedPSOmodelforsolvingtheoptimalformationreconfigurationcontrolproblemformultipleUCAVs.Firstly,theControlParameteri-zationandTimeDiscretization(CPTD)methodisdesignedindetail.Then,themutationstrategyandaspecialmutation-escapeoperatorareadoptedintheimprovedPSOmodeltomakeparticlesexplorethesearchspacemoreefficiently.Theproposedstrategycanproducealargespeedvaluedynamicallyaccordingtothevariationofthespeed,whichmakesthealgorithmexplorethelocalandglobalminimathoroughlyatthesametime.SeriesexperimentalresultsdemonstratethefeasibilityandeffectivenessoftheproposedmethodinsolvingtheoptimalformationreconfigurationcontrolproblemformultipleUCAVs.