一种新型灰狼优化算法及其在5G频率选择表面设计中的应用（英文）-豆柴文库

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一种新型灰狼优化算法及其在5G频率选择表面设计中的应用（英文）
Title:ANovelGreyWolfOptimizationAlgorithmanditsApplicationin5GFrequencySpectrumDesign
Abstract:
Thefifth-generation(5G)wirelesscommunicationsystemhasemergedasapromisingtechnologytomeettheincreasingdemandsforhigh-speedandreliablewirelessconnectivity.Efficientlyselectingsuitablefrequencybandsin5Gfrequencyspectrumdesignplaysavitalroleinoptimizingtheperformanceofthecommunicationsystem.Thispaperpresentsanoveloptimizationalgorithm,calledtheGreyWolfOptimization(GWO)algorithm,andexploresitsapplicationinthedesignoffrequencyselectionfor5Gsystems.TheGWOalgorithmisinspiredbythesocialhierarchyandhuntingbehaviorofgreywolves,makingitanefficientoptimizationtechniqueinsolvingvariouscomplexoptimizationproblems.ThispaperappliestheGWOalgorithmforoptimizingthefrequencyselectionprocessin5Gsystems,aimingtoenhancespectrumefficiencyandmitigateinterferencechallenges.
Introduction:
Thedemandforhigh-speedconnectivityandreliablewirelesscommunicationhasledtothedevelopmentof5Gwirelessnetworks.Advancedwirelesstechnologies,suchasmassivemultiple-inputmultiple-output(MIMO)systemsandmillimeter-wave(mmWave)communication,requireeffectivefrequencyspectrumallocation.However,intelligentfrequencyspectrumdesignisachallengingtaskduetothediverserequirementsofdifferentapplicationsandthescarcityofavailablespectrumresources.Traditionalfrequencyselectionmethodsoftenfacelimitationsinachievingefficientspectrumutilization,networkcapacityenhancement,andminimizinginterference.Inthispaper,weproposeanoveloptimizationalgorithm,knownastheGreyWolfOptimization(GWO)algorithm,foroptimizingthefrequencyselectionin5Gsystems.
GreyWolfOptimizationAlgorithm:
TheGWOalgorithmisinspiredbythehierarchicalstructureandhuntingbehaviorofgreywolves.Itmimicstheinteractionsandhuntingpatternsamongalpha,beta,delta,andomegawolvestofindtheglobaloptimumofagivenproblem.Thealgorithmbeginswithaninitializationphase,wherethesearchspaceisdefined,andtheinitialpositionsofthevirtualwolvesarerandomlygenerated.Thealgorithmthenit