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基于均方误差的集粒子云自适应均衡算法(英文).docx

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基于均方误差的集粒子云自适应均衡算法(英文) Introduction TheCloud-basedAdaptiveEqualizationAlgorithmforParticleSwarmOptimizationBasedonMeanSquareErrorisakeyconsiderationtooptimizeParticleSwarmOptimization(PSO)algorithms.Thealgorithmisdesignedtoautomaticallyequalizetheselectionofsolutionsinthecloud,inaccordancewiththeMeanSquareError(MSE).ThealgorithmisadoptedasacomprehensivesolutionforimprovingthePSOalgorithm,withafocusonaddressingthefundamentalissuesofoptimization:theselectionandarrangementofsolutionelementsandthedistributionofcognitiveandsocialrelationshipfactors.ThispaperpresentsanoverviewandanalysisoftheCloud-basedAdaptiveEqualizationAlgorithmforParticleSwarmOptimizationBasedonMeanSquareError. Background Cloudcomputingisemergingtechnologythathasgainedtremendouspopularityatagloballevel.Cloud-basedplatformsoffermanyadvantages,suchascostsavings,increasedcomputingpower,high-performancecapabilities,flexibility,andscalability.Theincreasingpopularityofcloud-basedserviceshasalsoledtotheparallelemergenceofbigdatasets.Thesedatasetscomewithincreasingvolumes,velocity,andvariety,presentingsignificantchallengesfordataanalysisandoptimization.ParticleSwarmOptimization(PSO)algorithmsofferaviablesolutionforsolvingtheproblembyallowingdistributedoptimization,parallelprocessing,andreducingthechancesoflocaloptima,therebyimprovingthealgorithm'sconvergence. Despitethecriticaladvantagesofthealgorithm,thereareseveralconcernsthatneedtobeaddressed.OneofthemainproblemswithPSOalgorithmsisthattheydependheavilyontheabilitytoevenlydistributethesolutionsacrossthecloud.Thisdependency,ofequalizingsolutions,leadstotheneedforrunningandanalyzingmultipleoptimizationruns,whichcanbetime-consumingandresource-intensive. ProposedAlgorithm TheCloud-basedAdaptiveEqualizationAlgorithmforParticleSwarmOptimizationBasedonMeanSquareErroralgorithmoffersacustomizedsolutiontomitigatethechallengesassociatedwiththetraditionalPSOalgorithm.Thealgorithmcomprisesthreeseparatesteps:datapreprocessing,dataequalization,andoptimization. Thefirstste