Computational Intelligence and Metaheuristic Algorithms with Applications PDF
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The Scientific World Journal Computational Intelligence and Metaheuristic Algorithms with Applications Guest Editors: Xin-She Yang, Su Fong Chien, and Tiew On Ting Computational Intelligence and Metaheuristic Algorithms with Applications The Scientific World Journal Computational Intelligence and Metaheuristic Algorithms with Applications Guest Editors: Xin-She Yang, Su Fong Chien, and Tiew On Ting Copyright©2014HindawiPublishingCorporation.Allrightsreserved. Thisisaspecialissuepublishedin“TheScientificWorldJournal.”AllarticlesareopenaccessarticlesdistributedundertheCreativeCom- monsAttributionLicense,whichpermitsunrestricteduse,distribution,andreproductioninanymedium,providedtheoriginalworkis properlycited. Contents ComputationalIntelligenceandMetaheuristicAlgorithmswithApplications,Xin-SheYang, SuFongChien,andTiewOnTing Volume2014,ArticleID425853,4pages GeneNetworkBiologicalValidityBasedonGene-GeneInteractionRelevance, FranciscoGo´mez-VelaandNorbertoD´ıaz-D´ıaz Volume2014,ArticleID540679,11pages ComparingEvolutionaryStrategiesonaBiobjectiveCulturalAlgorithm,CarolinaLagos, BroderickCrawford,EnriqueCabrera,RicardoSoto,Jose´-MiguelRubio,andFernandoParedes Volume2014,ArticleID745921,10pages TowardsEnhancementofPerformanceofK-MeansClusteringUsingNature-InspiredOptimization Algorithms,SimonFong,SuashDeb,Xin-SheYang,andYanZhuang Volume2014,ArticleID564829,16pages CongestionControlforaFairPacketDeliveryinWSN:FromaComplexSystemPerspective, DanielaAguirre-Guerrero,RicardoMarcel´ın-Jime´nez,EnriqueRodriguez-Colina,andMichael Pascoe-Chalke Volume2014,ArticleID381305,12pages AVariableNeighborhoodWalksat-BasedAlgorithmforMAX-SATProblems,NoureddineBouhmala Volume2014,ArticleID798323,11pages TuningofKalmanFilterParametersviaGeneticAlgorithmforState-of-ChargeEstimationinBattery ManagementSystem,T.O.Ting,KaLokMan,EngGeeLim,andMarkLeach Volume2014,ArticleID176052,11pages ImprovedBatAlgorithmAppliedtoMultilevelImageThresholding,AdisAlihodzicandMilanTuba Volume2014,ArticleID176718,16pages FocusingontheGoldenBallMetaheuristic:AnExtendedStudyonaWiderSetofProblems,E.Osaba, F.Diaz,R.Carballedo,E.Onieva,andA.Perallos Volume2014,ArticleID563259,17pages MultiobjectiveMemeticEstimationofDistributionAlgorithmBasedonanIncrementalTournament LocalSearcher,KaifengYang,LiMu,DongdongYang,FengZou,LeiWang,andQiaoyongJiang Volume2014,ArticleID836272,21pages NullSteeringofAdaptiveBeamformingUsingLinearConstraintMinimumVarianceAssistedby ParticleSwarmOptimization,DynamicMutatedArtificialImmuneSystem,andGravitationalSearch Algorithm,SoodabehDarzi,TiongSiehKiong,MohammadTariqulIslam,MahamodIsmail, SalehinKibria,andBalasemSalem Volume2014,ArticleID724639,10pages ACuckooSearchAlgorithmforMultimodalOptimization,ErikCuevasandAdolfoReyna-Orta Volume2014,ArticleID497514,20pages MACProtocolforAdHocNetworksUsingaGeneticAlgorithm,OmarElizarraras,MarcoPanduro, AldoL.Me´ndez,andAlbertoReyna Volume2014,ArticleID670190,9pages AnAntColonyOptimizationBasedFeatureSelectionforWebPageClassification, EsraSarac¸andSelmaAy¸seO¨zel Volume2014,ArticleID649260,16pages ReinforcementLearningforRoutinginCognitiveRadioAdHocNetworks,HasanA.A.Al-Rawi, Kok-LimAlvinYau,HafizalMohamad,NordinRamli,andWahidahHashim Volume2014,ArticleID960584,22pages FeaturesExtractionofFlotationFrothImagesandBPNeuralNetworkSoft-SensorModelof ConcentrateGradeOptimizedbyShuffledCuckooSearchingAlgorithm,Jie-shengWang,ShuangHan, Na-naShen,andShu-xiaLi Volume2014,ArticleID208094,17pages ANovelUserClassificationMethodforFemtocellNetworkbyUsingAffinityPropagationAlgorithm andArtificialNeuralNetwork,AfazUddinAhmed,MohammadTariqulIslam,MahamodIsmail, SalehinKibria,andHaslinaArshad Volume2014,ArticleID253787,14pages ASynchronous-AsynchronousParticleSwarmOptimisationAlgorithm,NorAzlinaAbAziz, MarizanMubin,MohdSaberiMohamad,andKamarulzamanAbAziz Volume2014,ArticleID123019,17pages GaitSignalAnalysiswithSimilarityMeasure,SanghyukLeeandSeungsooShin Volume2014,ArticleID136018,8pages AnImprovedAntColonyOptimizationApproachforOptimizationofProcessPlanning,JinFengWang, XiaoLiangFan,andHaiminDing Volume2014,ArticleID294513,15pages IntegratedModelofMultipleKernelLearningandDifferentialEvolutionforEUR/USDTrading, ShangkunDengandAkitoSakurai Volume2014,ArticleID914641,12pages ADistributedParallelGeneticAlgorithmofPlacementStrategyforVirtualMachinesDeploymenton CloudPlatform,Yu-ShuangDong,Gao-ChaoXu,andXiao-DongFu Volume2014,ArticleID259139,12pages AnArtificialBeeColonyAlgorithmforUncertainPortfolioSelection,WeiChen Volume2014,ArticleID578182,12pages FaultDetectionofAircraftSystemwithRandomForestAlgorithmandSimilarityMeasure,Sanghyuk Lee,WookjePark,andSikhangJung Volume2014,ArticleID727359,7pages AdaptiveMANETMultipathRoutingAlgorithmBasedontheSimulatedAnnealingApproach, SungwookKim Volume2014,ArticleID872526,8pages ASolutionQualityAssessmentMethodforSwarmIntelligenceOptimizationAlgorithms, ZhaojunZhang,Gai-GeWang,KuanshengZou,andJianhuaZhang Volume2014,ArticleID183809,8pages ApplicationofReinforcementLearninginCognitiveRadioNetworks:ModelsandAlgorithms, Kok-LimAlvinYau,Geong-SenPoh,SuFongChien,andHasanA.A.Al-Rawi Volume2014,ArticleID209810,23pages FireflyAlgorithmforCardinalityConstrainedMean-VariancePortfolioOptimizationProblemwith EntropyDiversityConstraint,NebojsaBacaninandMilanTuba Volume2014,ArticleID721521,16pages CuckooSearchwithLe´vyFlightsforWeightedBayesianEnergyFunctionalOptimizationin Global-SupportCurveDataFitting,AkemiGa´lvez,Andre´sIglesias,andLuisCabellos Volume2014,ArticleID138760,11pages PSO-BasedSupportVectorMachinewithCuckooSearchTechniqueforClinicalDiseaseDiagnoses, XiaoyongLiuandHuiFu Volume2014,ArticleID548483,7pages AnIslandGroupingGeneticAlgorithmforFuzzyPartitioningProblems,S.Salcedo-Sanz,J.DelSer, andZ.W.Geem Volume2014,ArticleID916371,15pages OntheEffectivenessofNature-InspiredMetaheuristicAlgorithmsforPerformingPhaseEquilibrium ThermodynamicCalculations,Seif-EddeenK.FateenandAdrianBonilla-Petriciolet Volume2014,ArticleID374510,12pages CloudModelBatAlgorithm,YongquanZhou,JianXie,LiangliangLi,andMingzhiMa Volume2014,ArticleID237102,11pages EvolutionaryMultiobjectiveQueryWorkloadOptimizationofCloudDataWarehouses, TanselDokeroglu,SeyyitAlperSert,andMuhammetSerkanCinar Volume2014,ArticleID435254,16pages AnInvestigationofGeneralizedDifferentialEvolutionMetaheuristicforMultiobjectiveOptimal Crop-MixPlanningDecision,OluwoleAdekanmbi,OludayoOlugbara,andJosiahAdeyemo Volume2014,ArticleID258749,8pages TowardstheNovelReasoningamongParticlesinPSObytheUseofRDFandSPARQL,IztokFisterJr., Xin-SheYang,KarinLjubicˇ,DuˇsanFister,JanezBrest,andIztokFister Volume2014,ArticleID121782,10pages SupportVectorMachineBasedonAdaptiveAccelerationParticleSwarmOptimization, MohammedHasanAbdulameer,SitiNorulHudaSheikhAbdullah,andZulaihaAliOthman Volume2014,ArticleID835607,8pages NovelBackPropagationOptimizationbyCuckooSearchAlgorithm,Jiao-hongYi,Wei-hongXu, andYuan-taoChen Volume2014,ArticleID878262,8pages Hindawi Publishing Corporation e Scientific World Journal Volume 2014, Article ID 425853, 4 pages http://dx.doi.org/10.1155/2014/425853 Editorial Computational Intelligence and Metaheuristic Algorithms with Applications Xin-SheYang,1SuFongChien,2andTiewOnTing3 1SchoolofScienceandTechnology,MiddlesexUniversity,LondonNW44BT,UK 2StrategicAdvancedResearch(StAR),MathematicalModelingLab,MIMOSBerhad,TechnologyParkMalaysia, KualaLumpur,Malaysia 3DepartmentofElectricalandElectronicEngineering,Xi’anJiaotong-LiverpoolUniversity,No.111Ren’aiRoad, HET,SIP,Suzhou,Jiangsu215123,China CorrespondenceshouldbeaddressedtoXin-SheYang;[email protected] Received4August2014;Accepted4August2014;Published31December2014 Copyright©2014Xin-SheYangetal.ThisisanopenaccessarticledistributedundertheCreativeCommonsAttributionLicense, whichpermitsunrestricteduse,distribution,andreproductioninanymedium,providedtheoriginalworkisproperlycited. 1.Introduction inmimickingnaturalsystems[1,2].Consequently,different algorithmsmayhavedifferentfeaturesandthusmaybehave Nature-inspiredmetaheuristicalgorithmshavebecomepow- differently, even with different efficiencies. However, It still erful and popular in computational intelligence and many lacksin-depthunderstandingwhythesealgorithmsworkwell applications. There are some important developments in andexactlyunderwhatconditions,thoughthereweresome recent years, and this special issue aims to provide a timely goodstudiesthatmayprovideinsightintoalgorithms[2,8]. review of such developments, including ant colony opti- Thisspecialissuefocusesontherecentdevelopmentsof mization,batalgorithm,cuckoosearch,particleswarmopti- SI-based metaheuristic algorithms and their diverse appli- mization,geneticalgorithms,supportvectormachine,neural cations as well as theoretical studies. Therefore, this paper networks,andothers.Inaddition,thesealgorithmshavebeen is organized as follows. Section2 provides an introduction appliedinadiverserangeofapplications,andsomeofthese and comparison of the so-called infinite monkey theorem latestapplicationsarealsosummarizedhere. and metaheuristics, followed by the brief review of compu- Computationalintelligenceandmetaheuristicalgorithms tational intelligence and metaheuristics in Section3. Then, havebecomeincreasinglypopularincomputerscience,arti- Section4 touches briefly the state-of-the-art developments, ficialintelligence,machinelearning,engineeringdesign,data and finally, Section5 provides some open problems about mining, image processing, and data-intensive applications. some key issues concerning computational intelligence and Mostalgorithmsincomputationalintelligenceandoptimiza- metaheuristics. tionarebasedonswarmintelligence(SI)[1,2].Forexample, bothparticleswarmoptimization[1]andcuckoosearch[3] 2.Monkeys,Shakespeare,andMetaheuristics have attracted much attention in science and engineering. Theybothcaneffectivelydealwithcontinuousproblems[2] Thereisawell-knownthoughtexperiment,calledtheinfinite and combinatorial problems [4]. These algorithms are very monkey theorem, which states that the probability of pro- differentfromtheconventionalevolutionaryalgorithmssuch ducinganygiventextwillalmostsurelybeoneifaninfinite as genetic algorithms and simulated annealing [5, 6] and numberofmonkeysrandomlytypeforaninfinitelylongtime otherheuristics[7]. [9,10].Inotherwords,theinfinitemonkeyscanbeexpected Manynewoptimizationalgorithmsarebasedontheso- toreproducethewholeworksofShakespeare.Forexample, called swarm intelligence (SI) with diverse characteristics to reproduce the text “swarm intelligence” (18 characters 2 TheScientificWorldJournal including the space), for a random typing sequence of 𝑛 methods,anddataminingtechniquesareallwellestablished, charactersona101-keycomputerkeyboard,theprobabilityof though constant improvements and refinements are being aconsecutive18-characterrandomstringtobe“swarmintel- carriedout.Forexample,neuralnetworksandsupportvector ligence”is𝑝𝑠 = (1/101)18 ≈ 8.4×10−37,whichisextremely machineshavebeenaroundforafewdecades,andtheyhave small.However,theimportancehereisthatthisprobability beenappliedtoalmosteveryareaofscienceandengineering is not zero. Therefore, for an infinitely long sequence 𝑛 → [12, 13]. However, it was mainly in the 1990s when these ∞, the probability of reproducing the collected works of twomethodsbecametrulypopular,whenthemasscomputer Shakespeareisone,thoughtheformalrigorousmathematical facilities become affordable with the steady increase of the analysisrequiresBorel-Cantellilemma[9,11]. computationalspeed. Conversely, we can propose a finite monkey theorem Nowadayscomputationalintelligencehaspermeatedinto withoutproof.Foragivenfinitenumberofmonkeystyping manyapplicationsdirectlyorindirectly.Accompanyingthis for a fixed amount of time, what is the probability of expansion,nature-inspiredmetaheuristicalgorithmsbeginto reproducinganypieceoftextsuchasthispaper? demonstratepromisingpowerincomputationalintelligence In many ways, heuristic and metaheuristic algorithms and many other areas [14]. For example, cuckoo search has havesomesimilaritiestotheinfinitemonkeyapproach.Mon- been used in optimizing truss structures [15] and other keystyperandomlyand,ultimately,somemeaningfulhigh- applications[3],whileahybridapproachcombiningatwo- qualitytextmayappear.Similarly,moststochasticalgorithms stage eagle strategy with differential evolution can save use randomization to increase the search capability. If such computational efforts [16]. New algorithms emerge almost algorithms are executed for a sufficiently long time with everyyearwithatrendofspeedup. multiple runs, it can be expected that the global optimality Algorithmswhichappearedinthelastfiveyearsinclude of a given problem can be reached or found. In theory, it bat algorithm [17], cuckoo search [3], flower pollination may take infinitely long to guarantee such optimality, but, algorithm [18], and others, which are in addition to the in practice, it can take many thousands or even millions of popularandwell-acceptedalgorithmssuchasparticleswarm iterations.Ifweconsidertheoptimalityasanimportantpiece optimization,antcolonyoptimization,fireflyalgorithm,dif- ofworkofShakespeare,theinfinitemonkeysshouldbeable ferential evolution, and genetic algorithms. Different algo- toreproduceorachieveitinaninfiniteamountoftime. rithms have different sources of inspiration, and they can However, there are some key differences between the also perform differently [1, 2]. For example, among most heuristicalgorithmsandtheinfinitemonkeyapproach.First, recent,bioinspiredalgorithms,flowerpollinationalgorithm monkeys randomly type without any memory or learning (FPA),orfloweralgorithm(FA)forsimplicity,wasdeveloped processing, and each key input is independent of another. by Xin-She Yang, which has demonstrated very good effi- Heuristic algorithms try to learn from history and the past ciencyinsolvingbothsingleoptimizationandmultiobjective moves so as to generate new, better moves or solutions optimization problems [18]. Both the flower algorithm and [7]. Second, random monkeys do not select what has been the cuckoo search use more subtle Le´vy flights instead of typed, while algorithms try to select the best solutions or standardGaussianrandomwalks[19]. thefittestsolutions[5].Third,monkeysusepurelystochastic However,someefficientapproachescanbebasedonthe components, while all heuristic algorithms use both deter- combinationofdifferentalgorithms,andtheeaglestrategyis ministicandstochasticcomponents.Finally,monkeytyping atwo-stagestrategycombiningacoarseexplorativestageand atmostisequivalenttoarandomsearchonaflatlandscape, anintensiveexploitativestageinaniterativemanner[16]. while heuristic algorithms are often cleverly constructed to Applications can be very diverse, from structural opti- use the landscape information in combination with history mization [15] to energy efficient telecommunications [20]. (memory) and selection. All these differences ensure that Detailed list of applications can be found in recent review heuristicalgorithmsarefarbetterthantherandommonkey- articles[3,17]orbooks[2]. typingapproach. In addition, metaheuristics are usually considered as a 4.State-of-the-ArtDevelopments higher level of heuristics, because metaheuristic algorithms arenotsimpletrial-and-errorapproachesandmetaheuristics Asthedevelopmentsareactiveandextensive,itisnotpossible aredesignedtolearnfrompastsolutions,tobebiasedtowards tocoveragoodpartoftherecentadvancesinasinglespecial better moves, to select the best solutions, and to construct issue.Therefore,thisspecialissuecanonlyprovideatimely sophisticatedsearchmoves.Therefore,metaheuristicscanbe snapshotofthestate-of-the-artdevelopments.Theresponses muchbetterthanheuristicalgorithmsandcandefinitelybe tothisspecialissuewereoverwhelming,andmorethan100 farmoreefficientthanrandommonkey-typingapproaches. submissionswerereceived.Aftergoingthroughtherigorous peer-review process, 32 papers have been accepted for this 3.ComputationalIntelligence issue.Abriefsummaryofthesepapersisgivenbelow. andMetaheuristics E. Cuevas et al. provide a study of multimodal opti- mizationusingthecuckoosearchalgorithm,whileE.Sarac¸ Computational intelligence has been in active development and S. A. O¨zel carry out web page classification using ant for many years. Classical methods and algorithms such colony optimization and O. Elizarraras et al. obtain better as machine learning methods, classifications and cluster performanceinadhocnetworkusinggeneticalgorithms.In
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