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Studies in Computational Intelligence 1009 B. Vinoth Kumar Diego Oliva P. N. Suganthan Editors Differential Evolution: From Theory to Practice Studies in Computational Intelligence Volume 1009 SeriesEditor JanuszKacprzyk,PolishAcademyofSciences,Warsaw,Poland The series “Studies in Computational Intelligence” (SCI) publishes new developments and advances in the various areas of computational intelligence—quickly and 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 themethodologiesbehindthem.Theseriescontainsmonographs,lecturenotesand editedvolumesincomputationalintelligencespanningtheareasofneuralnetworks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems,andhybridintelligentsystems.Ofparticularvaluetoboththecontributors and the readership are the short publication timeframe and the world-wide distribution,whichenablebothwideandrapiddisseminationofresearchoutput. IndexedbySCOPUS,DBLP,WTIFrankfurteG,zbMATH,SCImago. AllbookspublishedintheseriesaresubmittedforconsiderationinWebofScience. Moreinformationaboutthisseriesathttps://link.springer.com/bookseries/7092 · · B. Vinoth Kumar Diego Oliva P. N. Suganthan Editors Differential Evolution: From Theory to Practice Editors B.VinothKumar DiegoOliva DepartmentofInformationTechnology CUCEI PSGCollegeofTechnology UniversidaddeGuadalajara Coimbatore,India Guadalajara,Jalisco,Mexico P.N.Suganthan DepartmentofElectricalandElectronic Engineering NanyangTechnologicalUniversity Singapore,Singapore ISSN1860-949X ISSN1860-9503 (electronic) StudiesinComputationalIntelligence ISBN978-981-16-8081-6 ISBN978-981-16-8082-3 (eBook) https://doi.org/10.1007/978-981-16-8082-3 ©TheEditor(s)(ifapplicable)andTheAuthor(s),underexclusivelicensetoSpringerNature SingaporePteLtd.2022 Thisworkissubjecttocopyright.AllrightsaresolelyandexclusivelylicensedbythePublisher,whether thewholeorpartofthematerialisconcerned,specificallytherightsoftranslation,reprinting,reuse ofillustrations,recitation,broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,and transmissionorinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilar ordissimilarmethodologynowknownorhereafterdeveloped. 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The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore Preface Differentialevolution(DE)isapopulation-basedmeta-heuristictechniqueforglobal optimization capable of handling non-differentiable, nonlinear, and multi-modal objectivefunctions.Manyadvanceshavebeenmaderecentlyindifferentialevolu- tion, from theory to applications. The objective of this book is to address and disseminatestate-of-the-artresearchanddevelopmentofdifferentialevolutionand itsrecentadvances,suchasthedevelopmentofadaptive,self-adaptive,andhybrid techniques.Thisbookcomprisesofcontributionswhichincludetheoreticaldevelop- mentsinDE,performancecomparisonsofDE,hybridDEapproaches,paralleland distributedDEformulti-objectiveoptimization;softwareimplementations;andreal- world applications. The prospective audience would be researchers, practitioners, andstudentsindisciplinessuchasoptimization,heuristics,operationsresearch,and naturalcomputing. Wehopethechapterspresentedwillinspirefutureresearchbothfromtheoretical andpracticalviewpointstospurfurtheradvancesinthefield.Abriefintroductionto eachchapterisasfollows. Chapter “Analysis of Structural Bias in Differential Evolution Configurations” deals with structural bias which is a form of bias where artifacts in the algorithm leadtoapreferencetoparticularregionsinthesearchspaceregardlessoftheobjective function.Inthischapter,authorssystematicallyevaluate10980differentialevolution configurationsonstructuralbias,identifytheconfigurationswhichcausesbias,and analyzetheresultstomakeclearrecommendationsonwhichconfigurationstouse. Chapter “Spherical Model of Population Dynamics in Differential Evolution” discusses about the population dynamics models of differential evolution (DE). SelectioninDEischallengingforanalyticalmodelingduetoitsgreedycharacter. Symmetriesofthesphericalfunctionallowforapproximatingitutilizingamoment generatingfunctionofaminimumoftwonormallydistributedrandomvariables.This chapter describes the expected population diversity change in a complete iteration ofDE. Chapter“ReinforcementLearning-BasedDifferentialEvolutionforGlobalOpti- mization” proposes a reinforcement learning differential evolution where the reinforcement learning mechanism selects among the strategies incorporated from v vi Preface the original L-SHADE algorithm using the “DE/current-to-pbest/1/bin” mutation strategy toward the iL-SHADE to jSO using the “DE/current-to-pbest-w/1/bin” mutationstrategies. Chapter“AnalyticalStudyontheRoleofScaleFactorParameterofDifferential EvolutionAlgorithmonItsConvergenceNature”aimstoanalyzetheeffectofscale factor parameter (F) of Differential Evolution (DE) algorithm and to identify the relationshipbetweenF andnatureofconvergence.Basedontheanalysis,itusesan adaptationschemeforF.TheperformanceofDEwithadaptiveFisvalidatedonan imagesegmentationapplication. Chapter“TheTrapofSisypheanWorkinDifferentialEvolutionandHowtoAvoid It”providesthedetailedstudyaboutthestudytheproblemoftheoccurrenceofdupli- catesduringtheexecutionofelevenwell-knownDifferentialEvolutionalgorithms andtheArtificialBeeColonyalgorithmusingCEC2015benchmarkfunctionsand thestaticEconomicLoadDispatchproblem. Chapter “Investigations on Distributed Differential Evolution Framework withFaultToleranceMechanisms”presentsaninvestigationonfaulttolerancecapa- bilityofthedistributedislandmodelwhenframedwithdifferentialevolution(DE) algorithm.Thedistributeddifferentialevolution(dDE)algorithmicframeworkwas implemented and tested on three possible faulty scenarios which were simulated in the algorithm level. The dDE framework with and without the simulated faulty scenariosareexperimentedinsolvingbenchmarkingfunctionsfromCEC-2017.An empiricalcomparativestudywascarriedoutwiththeperformancemetricsdepicting thesolutionqualityandspeedoftheframework. Chapter“DifferentialEvolutionforWaterManagementProblems”appliesdiffer- ential evolution (DE) to solve two different water management problems, namely waterdistributionproblemandwaterreservoiroperationsproblem.Theresultsare comparedwithothermeta-heuristics,andDEoutperformedalltheotheralgorithms. Chapter“SobolSequence-basedMOSaDEAlgorithmforMulti-objectiveDesign ofWaterDistributionNetworks”presentstheSobolsequence-basedmulti-objective self-adaptive differential evolution (S-MOSADE) algorithm for reliability-based design of water distribution networks (WDNs), and performance is evaluated by comparingwithNSGA-IIalgorithm.ThestudyfindsthatS-MOSADEleadstobetter performancethanNSGA-II. Chapter “A Comparative Study on Parameter Estimation of COVID Epidemio- logicalModelsUsingDifferentialEvolutionAlgorithm”focusesonfindingoptimal parametersusingdifferentialevolutionforexistingCOVIDepidemiologicalmodels. Itusestheparametersforforecastingfuturescenariosandalsocomparesepidemi- ological models with different machine learning models based on evaluation techniques. Chapter “Applications of Differential Evolution in Electric Power Systems” discussesabouttheapplicationsofdifferentialevolutionanditsvariantsrelatedto powersystemproblemslikereactivepowerplanning,congestionmanagement,avail- abletransfercapability,loaddispatchineconomicalway,commitmentofgenerating units,optimizationofpowerflow,andoptimalreactivedispatchofelectricpower. Preface vii Chapter“DetectionofHeavySandstormRegionsUsingCompositeDifferential EvolutionAlgorithm”proposesacompositedifferentialevolutionalgorithm(CODE) toperformthesegmentationonthesatelliteimages. Chapter “A Hybrid Artificial Differential Evolution Gorilla Troops Optimizer for High-Dimensional Optimization Problems” proposes a new hybrid algorithm andartificialdifferentialevolutiongorillatroopsoptimizer(ADEGTO)forsolving high-dimensional optimization problems. ADEGTO uses the explorative power of the differential evolution algorithm and the exploitative power of the artificial gorilla troops optimizer. ADEGTO produced good solutions on high-dimensional optimizationproblemsandoutperforms10state-of-the-artalgorithms. Chapter “Multi-objective Adaptive Guided Differential Evolution for Multi-objective Optimal Power Flow Incorporating Wind-Solar-Small Hydro-Tidal Energy Sources”providesthedetailedstudywhichaimstosolvethemulti-objectiveoptimal powerflow(MOOPF)involvingrenewableenergysourcessuchaswind,solar,smallhydro, and tidal systems by using the multi-objective adaptive guided differential evolution(MOAGDE)algorithmunderdifferentoperatingconditions. Chapter“ApplicationsandPerformanceofFuzzyDifferentialEvolution(DEFIS) in CFD Modeling of Heat and Mass Transfer” presents the artificial intelligence (AI)learningtechniquesforsoftcomputinganddataoptimizationgeneratedbythe computationalfluiddynamics(CFD)approachformodelingfluiddynamicandheat transferphenomenon.Forthispurpose,theapplicationsandperformanceofahybrid algorithmofdifferentialevolutionandfuzzyinferencesystem(DEFIS)areexplained. Wearegratefultotheauthorsandreviewers fortheirexcellent contributions to makingthisbookpossible.OurspecialthanksgotoProf.Dr.JanuszKacprzyk(Series EditorofStudiesinComputationalIntelligence)fortheopportunitytoorganizethis editedvolume. We are grateful to Springer, especially to Mr. Aninda Bose (Senior Editor), for theexcellentcollaboration. Thiseditedbookcoversthefundamentalconceptsandapplicationareasindetail whichisoneofthemainadvantagesofthisbook.Beinganinterdisciplinarybook,we hopeitwillbeusefultoawidevarietyofreadersandwillprovideusefulinformation toprofessors,researchers,andstudents. Coimbatore,India Dr.B.VinothKumar Guadalajara,Mexico Dr.DiegoOliva Singapore Dr.P.N.Suganthan September2021 Contents AnalysisofStructuralBiasinDifferentialEvolutionConfigurations .... 1 DiederickVermetten, BasvanStein, AnnaV.Kononova, andFabioCaraffini SphericalModelofPopulationDynamicsinDifferentialEvolution ..... 23 KarolR.Opara ReinforcementLearning-BasedDifferentialEvolutionforGlobal Optimization ...................................................... 43 IztokFister,DušanFister,andIztokFisterJr. Analytical Study on the Role of Scale Factor Parameter ofDifferentialEvolutionAlgorithmonItsConvergenceNature ........ 77 DhanyaM.Dhanalakshmy, G.Jeyakumar, andC.ShunmugaVelayutham TheTrapofSisypheanWorkinDifferentialEvolutionandHow toAvoidIt ........................................................ 137 MatejCˇrepinšek,Shih-HsiLiu,MarjanMernik,andMihaRavber InvestigationsonDistributedDifferentialEvolutionFramework withFaultToleranceMechanisms ................................... 175 S.RaghulandG.Jeyakumar DifferentialEvolutionforWaterManagementProblems .............. 197 BilalandMilliePant SobolSequence-basedMOSaDEAlgorithmforMulti-objective DesignofWaterDistributionNetworks .............................. 215 SwatiSirsantandManneJangaReddy A Comparative Study on Parameter Estimation of COVID EpidemiologicalModelsUsingDifferentialEvolutionAlgorithm ....... 241 SaiSudhaPanigrahi, ArulJayanthMuthukumar, S.Thangavelu, G.Jeyakumar,andC.ShunmugaVelayutham ix x Contents ApplicationsofDifferentialEvolutioninElectricPowerSystems ....... 265 LukeJebaraj Detection of Heavy Sandstorm Regions Using Composite DifferentialEvolutionAlgorithm .................................... 297 MeeraRamadasandAjithAbraham A Hybrid Artificial Differential Evolution Gorilla Troops OptimizerforHigh-DimensionalOptimizationProblems .............. 315 AhmetCevahirCinar Multi-objective Adaptive Guided Differential Evolution for Multi-objective Optimal Power Flow Incorporating Wind-Solar-SmallHydro-TidalEnergySources ...................... 341 HamdiTolgaKahramanandSerhatDuman ApplicationsandPerformanceofFuzzyDifferentialEvolution (DEFIS)inCFDModelingofHeatandMassTransfer ................ 367 MeisamBabanezhadandImanBehroyan