Advances in Swarm Intelligence. Variations and Adaptations for Optimization Problems PDF
Preview Advances in Swarm Intelligence. Variations and Adaptations for Optimization Problems
Studies in Computational Intelligence 1054 Anupam Biswas Can B. Kalayci Seyedali Mirjalili Editors Advances in Swarm Intelligence Variations and Adaptations for Optimization Problems Studies in Computational Intelligence Volume 1054 SeriesEditor JanuszKacprzyk,PolishAcademyofSciences,Warsaw,Poland The series “Studies in Computational Intelligence” (SCI) publishes new develop- mentsandadvancesinthevariousareasofcomputationalintelligence—quicklyand with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, whichenablebothwideandrapiddisseminationofresearchoutput. ThisseriesalsopublishesOpenAccessbooks.ArecentexampleisthebookSwan, Nivel, Kant, Hedges, Atkinson, Steunebrink: The Road to General Intelligence https://link.springer.com/book/10.1007/978-3-031-08020-3 IndexedbySCOPUS,DBLP,WTIFrankfurteG,zbMATH,SCImago. AllbookspublishedintheseriesaresubmittedforconsiderationinWebofScience. · · Anupam Biswas Can B. Kalayci Seyedali Mirjalili Editors Advances in Swarm Intelligence Variations and Adaptations for Optimization Problems Editors AnupamBiswas CanB.Kalayci DepartmentofComputerScience DepartmentofIndustrialEngineering andEngineering PamukkaleUniversity NationalInstituteOfTechnologySilchar Pamukkale,Turkey Cachar,Assam,India SeyedaliMirjalili CentreforArtificialIntelligenceResearch andOptimisation TorrensUniversityAustralia Brisbane,QLD,Australia UniversityResearchandInnovationCenter ObudaUniversity Budapest,Hungary ISSN 1860-949X ISSN 1860-9503 (electronic) StudiesinComputationalIntelligence ISBN 978-3-031-09834-5 ISBN 978-3-031-09835-2 (eBook) https://doi.org/10.1007/978-3-031-09835-2 ©TheEditor(s)(ifapplicable)andTheAuthor(s),underexclusivelicensetoSpringerNature SwitzerlandAG2023 Thisworkissubjecttocopyright.AllrightsaresolelyandexclusivelylicensedbythePublisher,whether thewholeorpartofthematerialisconcerned,specificallytherightsoftranslation,reprinting,reuse ofillustrations,recitation,broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,and transmissionorinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilar ordissimilarmethodologynowknownorhereafterdeveloped. Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublication doesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevant protectivelawsandregulationsandthereforefreeforgeneraluse. Thepublisher,theauthors,andtheeditorsaresafetoassumethattheadviceandinformationinthisbook arebelievedtobetrueandaccurateatthedateofpublication.Neitherthepublishernortheauthorsor theeditorsgiveawarranty,expressedorimplied,withrespecttothematerialcontainedhereinorforany errorsoromissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregardtojurisdictional claimsinpublishedmapsandinstitutionalaffiliations. ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland Preface SwarmIntelligence(SI)hasgrownsignificantly,bothfromtheperspectiveofalgo- rithmicdevelopmentandapplicationscoveringalmostalldisciplinesinbothscience andtechnology.Generallyspeaking,thealgorithmsthatcomeunderSIdomainare typically population-based meta-heuristics techniques and are mostly inspired by nature. This book strives to cover all the major SI techniques, including particle swarmoptimization,antcolonyoptimization,fireflyalgorithm,dragonflyalgorithm, cuckoosearch,andmanymore.DifferentvariantsofsuchSItechniqueshavebeen developed depending on applications and often hybridized with other techniques to improve performance. This also includes some of the variants and hybridized versionsofpopularlyusedSItechniques. Ontheapplicationfront,SItechniquesarewidelyusedinoptimizationproblems indifferentdomains,includingbutnotlimitedtoElectricalandPowerSystems,Elec- tronicsandCommunicationEngineering,MachineLearning,DeepLearning,Social Network Analysis, Pattern Recognition, Speech Processing, Image Processing, Bioinformatics, Health Informatics, Manufacturing and Operation Research, just tonameafew.ThisbookcomprisessomeofthemajorapplicationsofSItechniques indifferentdomainsasmentionedabove.Thebookfocusesontheadaptivenature ofSItechniquesfromthecontextofapplications.Forspecificproblems,howrepre- sentation of population is done, how objective function is designed, what kind of changesaredoneinSItechniqueitself,howtodealwithconstraints,howtomanage conflictingobjectives,alltheseissuesareaddressedinthebookfromtheviewpoint ofapplications. This book emphasizes the studies of existing SI techniques, their variants and applications.ThebookalsocontainsreviewsofnewdevelopmentsinSItechniques andhybridizations.Algorithm-specificstudiescoveringbasicintroductionandanal- ysisofkeycomponentsofthesealgorithms,suchasconvergence,balanceofsolu- tionaccuracy,computationalcosts,tuning,andcontrolofparameters.Application- specific studies incorporating the ways of designing objective functions, solution representation,andconstrainthandling.Thebookalsoincludesstudiesonapplication domain-specificadaptationsintheSItechniques. v vi Preface Thebookisorganizedintofourparts.PartIcoversstateoftheartinSI,which includesreviewsonSItechniques,variousoptimizationproblems,andperformance analysis of SI techniques. Part II covers applications of SI techniques in various engineeringproblems.PartIIIcomprisesapplicationsofSItechniquesindifferent machinelearningproblemssuchasclusteringandprediction.Thelastpartincludes severalotherapplicationsofSItechniques. Wecordiallythankalltheauthorsfortheirvaluablecontributions.Wealsothank thereviewersfortheirinputandvaluablesuggestions. Finally,wethankallthestakeholderswhohavecontributeddirectlyorindirectly tomakingthisbookasuccess. Silchar,India AnupamBiswas Pamukkale,Turkey CanB.Kalayci Brisbane,Australia SeyedaliMirjalili March2022 Contents State-of-the-Art A Brief Tutorial on Optimization Problems, Optimization Algorithms,Meta-Heuristics,andSwarmIntelligence ................. 3 SeyedaliMirjalili,CanB.Kalayci,andAnupamBiswas IntroductoryReviewofSwarmIntelligenceTechniques ............... 15 ThounaojamChinglemba, SoujanyoBiswas, DebashishMalakar, VivekMeena,DebojyotiSarkar,andAnupamBiswas Swarm Intelligence for Deep Learning: Concepts, Challenges andRecentTrends ................................................. 37 VandanaBharti,BhaskarBiswas,andKaushalKumarShukla AdvancesonParticleSwarmOptimizationinSolvingDiscrete OptimizationProblems ............................................ 59 M.A.H.Akhand,Md.MasudurRahman,andNazmulSiddique Performance Analysis of Hybrid Memory Based Dragonfly AlgorithminEngineeringProblems ................................. 89 SanjoyDebnath,RaviSinghKurmvanshi,andWasimArif EngineeringProblems Optimum Design and Tuning Applications in Structural EngineeringviaSwarmIntelligence ................................. 109 GebrailBekdas¸,SinanMelihNigdeli,andAylinEceKayabekir BeeColonyOptimizationwithApplicationsinTransportation Engineering ....................................................... 135 DušanTeodorovic´,MilošNikolic´,MilicaŠelmic´,andIvanaJovanovic´ ApplicationofSwarmBasedApproaches forElasticModulus PredictionofRecycledAggregateConcrete .......................... 153 HarishNarayanaandPrashanthJanardhan vii viii Contents GreyWolfOptimizer,WhaleOptimizationAlgorithm,andMoth FlameOptimizationforOptimizingPhotonicsCrystals ............... 169 SeyedMohammadMirjalili, SeyedehZahraMirjalili, NimaKhodadadi,VaclavSnasel,andSeyedaliMirjalili Intelligent and Reliable Cognitive 5G Networks Using Whale OptimizationTechniques ........................................... 181 JasiyaBashir,JavaidAhmadSheikh,andZahidA.Bhat MachineLearning AutomaticDataClusteringUsingFarmlandFertilityMetaheuristic Algorithm ........................................................ 199 FarhadSoleimanianGharehchopoghandHumanShayanfar A Comprehensive Review of the Firefly Algorithms for Data Clustering ........................................................ 217 MKAAriyaratneandTGIFernando AHybridAfricanVultureOptimizationAlgorithmandHarmony Search:AlgorithmandApplicationinClustering ..................... 241 FarhadSoleimanianGharehchopogh, BenyaminAbdollahzadeh, NimaKhodadadi,andSeyedaliMirjalili EstimationModelsforOptimumDesignofStructuralEngineering ProblemsviaSwarm-IntelligenceBasedAlgorithmsandArtificial NeuralNetworks .................................................. 255 MeldaYücel,SinanMelihNigdeli,andGebrailBekdas¸ A Novel Codebook Generation by Lévy Flight Based Firefly Algorithm ........................................................ 269 IlkerKilic Novel Chaotic Best Firefly Algorithm: COVID-19 Fake News DetectionApplication .............................................. 285 MiodragZivkovic, AleksandarPetrovic, K.Venkatachalam, IvanaStrumberger,HothefaShakerJassim,andNebojsaBacanin OtherApplications ArtificialBeeColonyandGeneticAlgorithmsforParameters EstimationofWeibullDistribution .................................. 309 MuhammetBurakKılıç GraphStructureOptimizationforAgentControlProblemsUsing ACO ............................................................. 327 MohamadRoshanzamir,MahdiRoshanzamir,NavidHoseiniIzadi, andMaziarPalhang Contents ix ABumbleBeesMatingOptimizationAlgorithmfortheDiscrete andDynamicBerthAllocationProblem ............................. 347 EleftheriosTsakirakis,MagdaleneMarinaki,andYannisMarinakis Applying the Population-Based Ant Colony Optimization totheDynamicVehicleRoutingProblem ............................ 369 MichalisMavrovouniotis, GeorgiosEllinas, IaêS.Bonilha, FelipeM.Müller,andMariosPolycarpou AnImprovedCuckooSearchAlgorithmfortheCapacitatedGreen VehicleRoutingProblem ........................................... 385 KenanKaragulandYusufSahin Multi-ObjectiveArtificialHummingbirdAlgorithm .................. 407 NimaKhodadadi, SeyedMohammadMirjalili, WeiguoZhao, ZhenxingZhang,LiyingWang,andSeyedaliMirjalili
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