LectureNotesinComputerScience9873
CommencedPublicationin1973
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DavidHutchison
LancasterUniversity,Lancaster,UK
TakeoKanade
CarnegieMellonUniversity,Pittsburgh,PA,USA
JosefKittler UniversityofSurrey,Guildford,UK
JonM.Kleinberg
CornellUniversity,Ithaca,NY,USA
FriedemannMattern
ETHZurich,Zurich,Switzerland
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StanfordUniversity,Stanford,CA,USA
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WeizmannInstituteofScience,Rehovot,Israel
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IndianInstituteofTechnology,Madras,India
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DougTygar UniversityofCalifornia,Berkeley,CA,USA
GerhardWeikum
MaxPlanckInstituteforInformatics,Saarbrücken,Germany
Moreinformationaboutthisseriesathttp://www.springer.com/series/7407
BijayaKetanPanigrahi
PonnuthuraiNagaratnamSuganthan
SwagatamDas • SureshChandraSatapathy(Eds.)
Swarm,Evolutionary, andMemeticComputing
6thInternationalConference,SEMCCO2015
Hyderabad,India,December18–19,2015
RevisedSelectedPapers
Editors BijayaKetanPanigrahi
IIT
NewDehli
India
PonnuthuraiNagaratnamSuganthan
NanyangTechnologicalUniversity
Singapore
Singapore
SwagatamDas
IndianStatisticalInstitute
Kolkata
India
SureshChandraSatapathy DepartmentofComputerScience
Engineering
AnilNeerukondaInstituteofTechnology andSciences
Visakhapatnam
India
ISSN0302-9743ISSN1611-3349(electronic)
LectureNotesinComputerScience
ISBN978-3-319-48958-2ISBN978-3-319-48959-9(eBook) DOI10.1007/978-3-319-48959-9
LibraryofCongressControlNumber:2016956619
LNCSSublibrary:SL1 – TheoreticalComputerScienceandGeneralIssues
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Preface
ThisLNCSvolumecontainsthepaperspresentedatthe6thSwarm,Evolutionaryand MemeticComputingConference(SEMCCO2015)heldduringDecember18–19,2015, atCMRTechnicalCampus,Hyderabad,India.SEMCCOisregardedasoneofthe prestigiousinternationalconferenceseriesthataimsatbringingtogetherresearchers fromacademiaandindustrytoreportandreviewthelatestprogressincutting-edge researchonswarm,evolutionary,memeticcomputing,andothernovelcomputing techniqueslikeneuralandfuzzycomputing,toexplorenewapplicationareas,todesign newbio-inspiredalgorithmsforsolvingspeci fichardoptimizationproblems,and fi nally toraiseawarenessofthesedomainsinawideraudienceofpractitioners.
SEMCCO2015received150papersubmissionsfrom12countriesacrosstheglobe. Afterarigorouspeer-reviewprocessinvolving400reviewsintotal,40full-length articleswereacceptedfororalpresentationattheconference.Thiscorrespondstoan acceptancerateof27%andisintendedformaintainingthehighstandardsofthe conferenceproceedings.ThepapersincludedinthisLNCSvolumecoverawiderange oftopicsinswarm,evolutionary,memetic,andotherintelligentcomputingalgorithms andtheirreal-worldapplicationsinproblemsselectedfromdiversedomainsofscience andengineering.
Theconferencefeaturedthefollowingdistinguishedkeynotespeakers:Dr. P.N.Suganthan,NTU,Singapore,andDr.RammohanMallipeddi,Kyungpook NationalUniversity,SouthKorea.
Wetakethisopportunitytothanktheauthorsofallsubmittedpapersfortheirhard work,adherencetothedeadlines,andpatiencewiththereviewprocess.Thequalityofa refereedvolumedependsmainlyontheexpertiseanddedicationofthereviewers.We areindebtedtotheProgramCommittee/TechnicalCommitteememberswhonotonly producedexcellentreviewsbutalsodidsointheshorttimeframesthattheyweregiven.
Wewouldalsoliketothankoursponsorsforprovidingallthelogisticsupportand financialassistance.First,weareindebtedtoManagementandAdministrations(faculty colleaguesandadministrativepersonnel)ofCMRTechnicalCampus,Hyderabad.We thankProf.CarlosA.CoelloCoello,andProfNikhilR.Pal,theGeneralChairs,for providingvaluableguidelinesandinspirationtoovercomevariousdifficultiesinthe processoforganizingthisconference.Wewouldalsoliketothanktheparticipantsof thisconference.Finally,wewouldliketothankallthevolunteersfortheirtireless effortsinmeetingthedeadlinesandarrangingeverydetailtomakesurethatthe conferencecouldrunsmoothly.Wehopethereadersoftheseproceedingsandthe participantsoftheconferencefoundthepapersandconferenceinspiringandenjoyable.
December2015BijayaKetanPanigrahi
P.N.Suganthan SwagatamDas S.C.Satpathy
Organization
GeneralChairs
NikhilR.PalIndianStatisticalInstitute,Kolkata,India
CarlosA.Coello Coello InstitutoPolitécnicoNacional,México
GeneralCo-chairs
SwagatamDasIndianStatisticalInstitute,Kolkata,India
B.K.PanigrahiIITDelhi,NewDelhi,India
ProgramChair
S.C.SatapathyAnilNeerukondaInstituteofTechnologyandSciences, Visakhapatnam,India
FinanceChair
SrujanRajuCMRTechnicalCampus,Hyderabad,India
SteeringCommitteeChair
P.N.SuganthanNTU,Singapore
SpecialSessionChairs
SanjoyDasKansasStateUniversity,Kansas,USA
ZhihuaCuiTaiyuanUniversityofScienceandTechnology,China
SamuelsonHongOrientalInstituteofTechnology,Taiwan
InternationalAdvisoryCommittee/TechnicalReviewCommittee
AlmoatazYoussefAbdelaziz,Egypt
AthanasiosV.Vasilakos,Athens,Greece AlexK.Qin,France
AmitKonar,India
AnupamShukla,India
AshishAnand,India BoyangQu,China
CarlosA.CoelloCoello,Mexico
ChilukuriK.Mohan,USA
DelinLuo,China
DipankarDasgupta,USA
D.K.Chaturvedi,India
DiptiSrinivasan,Singapore
FatihM.Tasgetiren,Turkey
FerranteNeri,Finland
FrankNeumann,Australia
FayzurRahman,Portugal
G.K.Venayagamoorthy,USA
GerardoBeni,USA
HaiBinDuan,China
HeitorSilvérioLopes,Brazil
HalinaKwasnicka,Poland HongYan,HongKong,SARChina
JavierDelSer,Spain
JaneJ.Liang,China
JanezBrest,Slovenia
Jeng-ShyangPan,Taiwan
JuanLuisFernándezMartínez,Spain
Jeng-ShyangPan,Taiwan
KalyanmoyDeb,India
K.Parsopoulos,Greece KayChenTan,Singapore
KeTang,China
K.ShantiSwarup,India LakhmiJain,Australia
LeandroDosSantosCoelho,Brazil LingWang,China
LingfengWang,China
M.A.Abido,SaudiArabia
M.K.Tiwari,India
MauriceClerc,France
MengJooEr,Singapore Meng-HiotLim,Singapore
M.F.Tasgetiren,Turkey
NamrataKhemka,USA N.Puhan,India
OscarCastillo,Mexico
Pei-ChannChang,Taiwan
PengShi,UK
QingfuZhang,UK
QuankePan,China
RafaelStubsParpinelli,Brazil
RammohanMallipeddi,Singapore
RoderichGross,UK
RuhulSarker,Australia
RichaSing,India
RobertKozma,USA
SureshSundaram,Singapore
S.Baskar,India
S.K.Udgata,India
S.S.Dash,India
S.S.Pattanaik,India
S.G.Ponnambalam,Malaysia
SaeidNahavandi,Australia
SamanHalgamuge,Australia
ShizhengZhao,Singapore
SachidanandaDehuri,Korea
SamuelsonW.Hong,Taiwan VincenzoPiuri,Italy
X.Z.Gao,Finland
YewSoonOng,Singapore
YingTan,China
YuchengDong,China
Contents
Self-adaptiveEnsembleDifferentialEvolutionwithSampledParameter ValuesforUnitCommitment..................................1
NandarLynn,RammohanMallipeddi, andPonnuthuraiNagaratnamSuganthan
EmpiricalAssessmentofHumanLearningPrinciplesInspiredPSO AlgorithmsonContinuousBlack-BoxOptimizationTestbed.............17
M.R.Tanweer,AbdullahAl-Dujaili,andS.Suresh
VisualCryptographyBasedLosslessWatermarkingforSensitiveImages....29 SurekhaBorra,ViswanadhaRajuS.,andLakshmiH.R.
CohortIntelligenceandGeneticAlgorithmAlongwithAHP toRecommendanIceCreamtoaDiabeticPatient...................40 SuhasMachhindraGaikwad,RahulRaghvendraJoshi, andAnandJayantKulkarni
Design,ConstructionandAnalysisofModelDatasetforIndianRoad NetworkandPerformingClassificationtoEstimateAccuracy ofDifferentClassifierwithItsComparisonSummaryEvaluation.........50 SuwarnaGothane,M.V.Sarode,andK.SrujanRaju
AHybridEMD-ANNModelforStockPricePrediction................60 DhanyaJothimani,RaviShankar,andSurendraS.Yadav
DevelopmentofBackPropagationNeuralNetwork(BPNN)Model toPredictCombustionParametersofDieselEngine...................71 M.ShailajaandA.V.SitaRamaRaju
AnImprovedQuantumInspiredImmuneCloneOptimizationAlgorithm....84 AnnavarapuChandraSekharaRao,SureshDara,andHaiderBanka
DiagnosisofParkinsonDiseasePatientsUsingEgyptianVulture OptimizationAlgorithm......................................92 AdityaDixit,AlokSharma,AnkurSingh,andAnupamShukla
VarianceBasedParticleSwarmOptimizationforFunctionOptimization andFeatureSelection........................................104 YamunaPrasad,K.K.Biswas,M.Hanmandlu, andChakreshKumarJain
AnalysisofNext-GenerationSequencingDataofmiRNAforthePrediction ofBreastCancer...........................................116
IndrajitSaha,ShibSankarBhowmick,FilippoGeraci,MarcoPellegrini, DebotoshBhattacharjee,UjjwalMaulik,andDariuszPlewczynski
GeneticAlgorithmBasedSpeedControlofElectricVehicle withElectronicDifferential....................................128
NairR.DeepthiandJ.L.FebinDaya
AnAntColonyOptimizationApproachfortheDominatingTreeProblem...143 ShyamSundar,SachchidaNandChaurasia,andAlokSingh
Multi-objectivePowerDispatchUsingStochasticFractalSearch AlgorithmandTOPSIS......................................154
HariMohanDubey,ManjareePandit,B.K.Panigrahi, andTusharTyagi
ParticleSwarmOptimizationfortheDeploymentofDirectionalSensors....167 PankajSingh,S.Mini,andKetanSabale
RegionBasedMultipleFeaturesforanEffectiveContentBasedAccess MedicalImageRetrievalanIntegratedwithRelevanceFeedbackApproach..176 B.Jyothi,Y.MadhaveeLatha,P.G.KrishnaMohan,andV.S.K.Reddy
RobotWorkcellLayoutOptimizationUsingFireflyAlgorithm...........188 AkifMuhtasimAlim,S.G.Ponnambalam,andG.Kanagaraj
ParticleSwarmOptimizationBasedontheWinner ’sStrategy............201 ShailendraS.Aote,M.M.Raghuwanshi,andL.G.Malik
BlackHoleArtificialBeeColonyAlgorithm.......................214 NirmalaSharma,HarishSharma,AjaySharma, andJagdishChandBansal
AGravitationalSearchAlgorithmforEnergyEfficientMulti-sink PlacementinWirelessSensorNetworks...........................222
P.C.SrinivasaRao,HaiderBanka,andPrasantaK.Jana
OptimumClusteringofActiveDistributionNetworksUsingBackTracking SearchAlgorithm..........................................235
RehamA.Osama,AlmoatazY.Abdelaziz,RaniaA.Swief, MohamedEzzat,R.K.Saket,andK.S.AnandKumar
EnergyEfficientClusteringforWirelessSensorNetworks: AGravitationalSearchAlgorithm...............................247
P.C.SrinivasaRao,HaiderBanka,andPrasantaK.Jana
HybridizingCuckooSearchwithBio-inspiredAlgorithmsforConstrained OptimizationProblems.......................................260 G.Kanagaraj,S.G.Ponnambalam,andA.H.Gandomi
AHybridGeneticAlgorithmUsingDynamicDistance inMutationOperatorforSolvingMSAProblem.....................274 RohitKumarYadavandHaiderBanka
AuthorIndex ............................................287
2LiteratureReview
2.1DifferentialEvolution
DifferentialEvolution(DE)beingaparalleldirectsearchmethodutilizes NP D-dimensionalparametervectors,so-calledindividuals,whichencodethecandidate solutions,i.e. Xi;G ¼ x1 i;G ; ; xD i;G no; i ¼ 1; ; NP G representsthegenerationcount. Theuniformrandomizationoftheinitialpopulationtriestocoverthesearchspace constrainedbytheprescribedminimumandmaximumparameterbounds, Xmin ¼ fx1 min ; ...; xDmin g and Xmax ¼fx1 max ; ...; xDmax g,asmuchaspossible.Forexample,the initialvalueofthe jth parameterofthe ith individualatgeneration G = 0 isgeneratedby:
where randj(0,1) isauniformlydistributedrandomvariableintherange[0,1)[19]. Afterinitialization,DEemploysmutationoperationtoproducemutantvector Vi,G correspondingtoeachindividual Xi,G,so-calledtargetvector,inthecurrentpopulation. Foreachtargetvector Xi,G ingeneration G,itsassociatedmutantvectorcanbegeneratedviamutationstrategy.Themostfrequentlyusedmutationstrategiesare[9]: “DE/best/1”:
DE/current-to-rand/1”:
Theindices r i 1 ; r i 2 ; r
3 ; r i 4 ; r i 5 aremutuallyexclusiveintegersrandomlygenerated anewforeachmutantvectorwithintherange[1, NP],whicharealsodifferentfromthe index i. Xbest,G isthebestindividualvectorwiththebest fitnessvalueinthepopulation atgeneration G. K israndomlychosenwithintherange[0,1].Intheaboveequations, thescalefactor F 2ð0; 1 þÞ [17]isapositiverealnumberthatcontrolstherateof
populationevolution.Therearevariousclaimsandcounter-claimsregardingthesuitablerangeofthe F values[17].However, F mustbeaboveacertaincriticalvalueto avoidprematureconvergence[6].Inaddition,alarger F increasestheprobabilityof escapingfromalocaloptimum[6].Butif F becomestoolarge,thenumberoffunction evaluationsto findtheoptimumgrowsveryquickly.In[18],itwastheoreticallyproved thatDEcouldconvergetoglobaloptimuminthelongtimelimitif F canbetransformedintoaGaussianrandomvariable.However,itwaslaterdemonstrated[17]that unlessthevarianceoftherandomizingdistributionisverysmall,DEwillsuffera signifi cantperformancelossonhighlyconditionednon-separablefunctions.
Afterthemutation,crossoveroperationisappliedtoeachpairofthetargetvector Xi,G anditscorrespondingmutantvector Vi,G togenerateatrialvector: Ui;G ¼fu1 i;G ; ...; uD i;G g.Thecrossoveroperationspeedstheconvergencebyaconstant factor[17].Inthebasicversion,DEemploysthebinomialcrossoverdefi nedasfollows:
InEq.(7),thecrossoverprobability,CR 2½0; 1 isauser-specifiedconstantthat controlsthefractionofparametervaluesthatarecopiedtothetrailvectorfromthe mutantvector. jrand isarandomlychosenintegerintherange[1, D].Thecrossoverrate (CR)isaprobability(0 ≤ CR ≤ 1)ofmixingbetweentrialandtargetvectorsand controlshowmanycomponentsaremutatedineachelementofthecurrentpopulation. In[6],variousguidelinestoselecttheappropriatevaluesforaproblem-at-handwere putforward.However,asimpleguidelinewhichstatesthatsmallvaluesaresuitablefor separableproblemswhilelargervaluesof CR aresuitableformulti-modal,parameter dependentproblems[17]iscommonlyused.
Ifgeneratedtrialvectorexceedsthecorrespondingupperandlowerbounds,itis randomlyanduniformlyreinitializedintherangeoflowerandupperbound.The objectivefunctionvaluesofalltrialvectorsareevaluated.Aftercrossoveroperation,a selectionoperationisperformed.The fitnessfunctionvalueofeachtrialvector f(Ui,G) iscomparedtothatofitscorrespondingtargetvector f(Xi,G).Ifthetrialvectorhasless orequal fitnessfunctionvalue(inaminimizationproblem),thetrialvectorwillreplace thetargetvectorandenterthepopulationofthenextgeneration.Otherwise,thetarget vectorwillremaininthepopulationforthenextgeneration.Theselectionoperation canbeexpressedasfollows:
The3steps(mutation,crossoverandselection)arerepeatedgenerationaftergenerationuntilaterminationcriterion(reachingthemaximumnumberoffunctionevaluationsset)issatis fied.
2.2LiteratureReview
AsDEissensitivetoparametersettings[7],toavoidmanualtuningofparameter settingsandtogetanoptimalperformance,severalvariantsofDEbasedontheadaptationandself-adaptationofmutationstrategiesandcontrolparametershavebeen proposed.DEmaysufferfromstagnationandprematureconvergenceduetoimproper selectionofcontrolparameters,parametersbeingkept fixedthroughthewholesearch process[19].ToovercometheseproblemsFADEwasproposedwhichadaptsthe controlparameters F and CR basedonfuzzylogiccontrollerswhoseinputsarethe relativefunctionvaluesandindividualsofsuccessivegenerationstoadapttheparametersforthemutationandcrossoveroperation[19].FADEenablestochoosetheinitial controlparametersfreelyandadjustthecontrolparameterson-linetodynamicallyadapt tochangingsituations.AparameteradaptationofDE(ADE)basedoncontrollingthe populationdiversityandamulti-populationapproachwasproposed[20].In[10],a self-adaptationscheme(SDE)inwhich CR isgeneratedrandomlyforeachindividual usinganormaldistribution N (0.5,0.15),while F isadaptedanalogoustotheadaptation ofcrossoverrate CR in[21].
DEcanencompassanumberoftrialvectorgenerationstrategies,eachofwhich maybeeffectiveovercertainproblemsbutpoorlyperformovertheothers.In[12],a self-adaptiveDEalgorithm(SaDE)wasproposedinwhichthemutationstrategiesand therespectivecontrolparameterareself-adaptedbasedontheirpreviousexperiencesof generatingpromisingsolutions. F wasrandomlygeneratedwithameanandstandard deviationof0.5and0.3respectively.InSaDE,foureffectivetrialvectorgeneration strategiesnamelytheDE/rand/1/bin,DE/rand-to-best/2/bin,DE/rand/2/binand fi nally DE/current-to-rand/1werechosentoconstituteastrategycandidatepool.InSaDE algorithm,foreachtargetvectorinthecurrentpopulation,onetrialvectorgeneration strategyisselectedfromthecandidatepoolaccordingtotheprobabilitylearnedfrom itssuccessrateingeneratingimprovedsolutionswithinacertainnumberofprevious generations,calledtheLearningPeriod(LP).Theselectedstrategyissubsequently appliedtothecorrespondingtargetvectortogenerateatrialvector.Morespecifi cally, ateachgeneration,theprobabilitiesofchoosingeachstrategyinthecandidatepoolare summedto1.Theseprobabilitiesareinitiallyequal(1/K for K strategiesinthepool) andarethengraduallyadaptedduringevolution,basedonthe SuccessandFailure Memories (nsk,g and nfk,g)overtheprevious LP generations.Theadaptationofthe probabilitiestakeplaceinsuchafashionthat,thelargerthesuccessrateforthe kth strategyinthepoolwithintheprevious LP generations,thelargeristheprobabilityof applyingittogeneratetrialvectorsatthecurrentgeneration.Theprobabilityof selectingthestrategies(k = 1,2… K)inthepoolisupdatedatthegeneration G using thefollowingequation:
where,
(k = 1,2,..,K;G > LP)
InEq.(10), Sk,G isthesuccessrateoftrailvectorsgeneratedbythe kth strategythat cansuccessfullyenterthenextgenerationwithinlearningperiod LP.Inordertoavoid thenullsuccessrate, ε =0.01isused.
InJADE[22],anewmutationstrategy “DE/current-to-pbest”,ageneralizationof theclassic “DE/current-to-best ” wasproposed.Thereliabilityofthealgorithmisfurtherimprovedbytheadaptiveparametercontrol. “DE/current-to-pbest” utilizesnot onlythebestsolutioninformationbutalsotheinformationofothergoodsolutions.
X p best ;G israndomlychosenasoneofthetop100p %individualsinthepopulationwith p 2ð0; 1 . Fi,G isthemutationfactorof Xi,G,generatedindependentlyateachgeneration G,accordingtoaCauchydistributionasfollows:
SF denotesthesetofallsuccessfulmutationfactorsingeneration G.Thelocation parameter µF isinitializedtobe0.5andthenupdatedattheendofeachgenerationas:
where, meanL(.)istheLehermean. CRi,G isthecrossoverprobabilityof Xi,G,generated independentlyateachgeneration G,accordingtoanormaldistributionasfollows:
SCR denotesthesetofallsuccessfulcrossoverprobabilitiesingeneration G.Themean µCR isinitializedtobe0.5andthenupdatedattheendofeachgenerationas:
where, meanA(.) istheusualarithmeticmean.JADEusuallyperformsbetterwith 1/c 2 [5, 20]and p 2 [5%,20%];i.e.,lifespanof µCR and µF valuesrangesfrom5to20 generationsandtop5–20%highqualitysolutionsareconsideredformutation.In[15], animprovedversionofJADEcalledsuccesshistorybasedadaptiveDE(SHADE) algorithmwasproposedinwhichhistory-basedparameteradaptationschemeisused. InSHADE, F and CR valuesaregeneratedusingEqs.(12)and(15)andmeanvalues ofsuccessful F and CR arestoredinhistoricalmemory.Linearpopulationsize reductionwasintroducedtoimprovethesearchperformanceofSHADEin[16].
3Self-adaptiveDEwithEnsembleofMutationStrategies andSampledParameterValues(Sa-EPSDE)
AnensembleofmutationstrategiesandparametervaluesforDE(EPSDE)inwhicha poolofmutationstrategies,alongwithapoolofvaluescorrespondingtoeachassociatedparametercompetestoproducesuccessfuloffspringpopulationwasproposed. Moreover,EPSDEisincorporatedwithaself-adaptiveframework[23]inwhichthe mutationandcrossoverstrategiesandparametersinthepoolaregraduallyself-adapted bylearningfromtheirpreviousrecordedperformance[12].InSa-EPSDE,the parametervaluesare fixeddiscretevalues.However,theperformanceoftheSa-EPSDE algorithmcanbefurtherenhancediftheparametervaluesusedduringevolution processaresampledfromthedistributionwithdifferentmeanvalues[15, 16]. Therefore,Sa-EPSDEismodifiedwiththevaluesof F and CR whicharesampledfrom fixedmeanvaluesusingEqs. 12 and 15,respectively.Inordertobalancethespeedand efficiencywhilesolvingproblemswithdifferentcharacteristics,thepoolof CR values istakenintherange0.1to0.9instepsof0.1.Basedontheliterature,thepoolof F valuesistakenintherange0.4to0.9instepsof0.1.Themutationstrategiesareused assameasinEPSDE[9].TheproposedSa-EPSDEalgorithmisdemonstratedbelow:
STEP 1: Set G=0,and randomly initialize NP individuals } X ,..., {X P , 1, G NP G G = with NP i x x D G i G i G i 1,..., }, ,..., { X , 1 , , = = uniformly distributed in the range [Xmin, Xmax], where } ,..., { X min 1 min min D x x = and } ,..., { X max 1 max max D x x = . Initialize strategy probability (pk,G, k=1,..., K; K is number of available strategies in the pool) and learning period LP
STEP 2: Select a pool of mutation strategies and a pool of values for each associated parameters corresponding to each mutation strategy.
STEP 3: Each population member is randomly assigned with one of the mutation strategy from the pool and the associated parameter values are chosen randomly from the corresponding pool of values.
STEP 4: WHILE stopping criterion is not satisfied
DO
FOR i = 1 to NP
STEP 4.1 Calculate strategy probability pk,G and update Success and Failure Memory (nsk,g and nfk,g)
IF G > LP
FOR k=1: K
update pk,G by equation (9) remove nsk,G-LP and nfk,G-LP out of Success and Failure Memory respectively
ENDFOR
ENDIF
STEP 4.2 Mutation:
Generate a mutated vector correspondingto the target vector Xi,G. Sample the F value to be used during the mutation using equation 12, where meanvalue to be used in equation 12is the discrete value selected from the pool.
STEP 4.3 Crossover
Generate a trial vector foreach target vector Xi,G. Sample the CR value to be used during the mutation using equation 15, where mean value to be used in equation 15is the discrete value selected from the pool.
STEP 4.4 Selection
Evaluate the trial vector Ui,G
IF f(Ui,G) ≤ f(Xi,G), THEN Xi,G+1 = Ui,G, f(Xi,G+1) ≤ f(Ui,G), nsk,G = nsk,G+1 IF f(Ui,G) < f(Xbest,G), THEN Xbest,G = Ui,G, f(Xbest,G) ≤ f(Ui,G) /* Xbest,G is the best individual in generation G */
END IF ELSE
Xi,G+1 = Xi,G, f(Xi,G+1) ≤ f(Xi,G), nfk,G = nfk,G+1
END IF
STEP 4.5 Updating IF f(Ui,G) > f(Xi,G), THEN randomlyselect a new mutation strategy and parameter values from the pools or from the stored successful combinations.
END IF
END FOR
Store nsk,G & nfk,G, k=1,..., K, into Success and Failure Memory respectively.
STEP 4.6 Increment the generation count G = G + 1
END WHILE
4ExperimentalStudyonNumericalBenchmarkProblems
Inthispaper,CEC2005benchmarkfunctionsfeaturingdifferentproperties (uni-modal/multi-modal,shifted,rotated,scalable,separable/non-separable)areusedto evaluatetheperformanceoftheproposedSa-EPSDEwith F and CR sampledfromthe distributionwith fixedmeanvaluesusingEqs. 12 and 15.TheproposedSa-EPSDE algorithmiscomparedwithSaDEandEPSDEandtheexperimentsareconductedon the first14benchmarkproblemsdescribedin[24].Intheexperiments,thepopulation NP issetto30andthreedifferentdimensionDsizes(10D,30Dand50D)aretested. Thenumberoffunctionevaluation(FES)issetup100,000for10Dproblems,300,000 for30Dproblemsand500,000for50Dproblems.Forallthealgorithms,theexperimentisrun25timesforeachproblem.Theexperimentresultsfor10D,30Dand50D areshowninTables 1, 2 and 3 respectivelyandthebestexperimentalresultsare highlightedinboldineachtable.
For10dimensionaluni-modalproblems,allthealgorithmsperformapproximately equalonfunction F1.TheproposedSa-EPSDEalgorithmoutperformsbestonfunctions F2, F4 and F5 andEPSDEoffersbestperformanceonfunction F3.For multi-modalproblems,SaDEperformanceisbeston F7, F11 and F14,EPSDEon F9 and F10 andSa-EPSDEonallotherproblems.Overall,comparedtoSaDEandEPSDE algorithms,proposedSa-EPSDEalgorithmprovidesbestperformanceon8outof14 10dimensionalproblems.
For30dimensionalproblemsinTable 2,comparedtoSaDE,theproposed Sa-EPSDEalgorithmoutperformson9outof14functionsandperformsapproximately equalontestfunction F8.ComparedtoEPSDEalgorithm,Sa-EPSDEalgorithmoffers
Table1. ComparisonbetweenSa-EPSDE,SaDEandEPSDEfor10Dproblems
Type Errormeans(F(x) – F(x*)) ± StandardDeviationfor10Dproblems FSaDEEPSDESa-EPSDE mean –stdmean –
Uni-modal F1
F2 1.48E–274.22E–271.92E–
–28
–
–28 F3 2.56E+043.44E+04 3.45E+011.24E+02 1.12E+033.09E+03
F4 1.37E–281.82E–284.06E–284.95E–28 1.11E–281.08E–28
F5 3.01E–081.36E–073.60E+014.28E+01 0.00E+000.00E+00
Multi-modal F6 5.57E+001.45E+013.19E–011.10E+00 6.78E–261.63E–25
F7 5.96E–024.57E–02 2.13E–012.04E–026.84E–024.42E–02
F8 2.04E+016.72E–022.04E+018.00E–022.02E+011.07E–01
F9 3.98E–021.99E–01 0.00E+000.00E+000.00E+000.00E+00
F10 7.08E+002.74E+00 6.02E+002.08E+00 1.24E+016.00E+00
F11 2.21E+001.09E+00 6.08E+009.79E–013.94E+001.66E+00
F12 1.96E+025.13E+022.22E+021.23E+02 2.27E+015.80E+01
F13 4.04E–011.25E–012.49E–013.81E–02 2.15E–018.25E–02
F14 2.47E+004.66E–01 3.51E+003.21E–013.06E+003.59E–01
Table2. ComparisonbetweenSa-EPSDE,SaDEandEPSDEfor30Dproblems
Type Errormeans(F(x) – F(x*)) ± StandardDeviationfor30Dproblems
FSaDEEPSDESa-EPSDE
mean –stdmean –stdmean –std
Uni-modal
F1 3.43E–291.03E–281.57E–294.22E–29 0.00E+000.00E+00
F2 9.60E–052.85E–041.78E–244.81E–24 1.27E–254.26E–25
F3 6.25E+052.12E+05 5.24E+043.91E+04 1.19E+056.46E+04
F4 6.77E+026.21E+021.47E+032.35E+03 1.15E+003.48E+00
F5 4.40E+036.74E+021.99E+037.75E+02 1.89E+038.34E+02
Multi-modal F6 4.84E+013.96E+011.12E+001.83E+00 2.69E–234.77E–23
F7 2.03E–021.52E–026.20E+004.40E+00 1.78E–021.84E–02
F8 2.09E+014.78E–022.10E+014.91E–022.07E+012.06E–01
F9 1.83E+001.74E+001.19E–013.30E–01 0.00E+000.00E+00
F10 5.76E+011.39E+01 5.38E+011.54E+01 5.97E+012.12E+01
F11 2.02E+013.17E+00 3.33E+014.25E+002.42E+013.52E+00
F12 3.23E+033.03E+033.58E+042.76E+04 2.80E+032.58E+03
F13 2.46E+008.81E–011.71E+004.85E–01 8.78E–011.70E–01
F14 1.23E+014.81E–01 1.35E+014.33E–011.25E+014.49E–01
Table3. ComparisonbetweenSa-EPSDE,SaDEandEPSDEfor50Dproblems
Type Errormeans(F(x) – F(x*)) ± StandardDeviationfor50Dproblems FSaDEEPSDESa-EPSDE mean –stdmean –stdmean –std
Uni-modal F1 1.89E–283.78E–282.82E–282.57E–28 0.00E+000.00E+00
F2 7.16E–011.60E+00 3.01E–191.43E–18 2.40E–135.97E–13
F3 1.19E+063.79E+051.44E+073.99E+07 3.81E+051.27E+05
F4 1.23E+045.24E+031.70E+041.56E+04 1.36E+031.88E+03
F5 1.16E+041.62E+03 6.43E+031.85E+03 7.37E+031.81E+03
Multi-modal F6 1.24E+021.10E+022.40E+002.00E+00 1.60E–197.45E–19
F7 4.14E–038.14E–03 1.05E+001.12E–015.31E–031.21E–02
F8 2.11E+013.23E–022.11E+014.07E–022.11E+016.83E–02
F9 1.05E+014.77E+009.15E–012.20E+00 2.39E–014.34E–01
F10 1.72E+023.21E+011.60E+023.65E+01 1.15E+022.77E+01
F11 4.33E+013.34E+00 7.16E+013.72E+004.89E+014.56E+00
F12 1.04E+046.20E+03 2.41E+054.07E+041.67E+041.16E+04
F13 4.04E+001.48E+005.29E+004.05E–01 1.50E+002.74E–01
F14 2.16E+016.68E–01 2.34E+013.72E–012.22E+015.78E–01
betterperformance10out14testfunctionsandperformsapproximatelyequalon function F2 and F8.SaDEperformsbetteronfunction F10, F11 and F14 andEPSDE doeson F3 and F10
For50dimensionalproblemsinTable 3,comparedtoSaDE,theproposed Sa-EPSDEalgorithmperformsbetteron9outof14problems.SaDEperformsbeston function F7, F11, F12 and F14.ComparedtoEPSDEalgorithm,Sa-EPSDEalgorithm providesbetterperformanceon11out14problems.EPSDEoutperformsSa-EPSDEon functions, F2 and F5.Thethreealgorithmsperformapproximatelyequalonfunction F8.Overall,Sa-EPSDEalgorithmoutperformsbeston7outof14uni-modaland multi-modal50Dproblems.
Inallthreedimensions,Sa-EPSDEalgorithmoutperformsSaDEandEPSDE algorithmsbyalargemarginsuchasinfunction F6 and F9.Thealgorithmperforms onlyslightlyworsethantheothertwoalgorithmsonfewproblemssuchasinfunction F11 and F12.Therefore,proposedSa-EPSDEalgorithmoffersbestperformanceonall 10D,30Dand50Dshiftedrotatedbenchmarkproblems.
5ExperimentalStudyonUnitCommitmentProblem
Unitcommitment(UC)problemisoneoftheimportantoptimizationproblemsinthe electricalpowersystem.UCproblemdealswithproducingtheoptimalscheduleof availablepowergeneratingunitsoveraschedulingperiodwhilesatisfyingloaddemand andspinningreserverequirementsattheminimumproductioncost.UCcanbedivided intotwosubproblems:unitscheduledsubproblemandeconomicdispatchsubproblem. Unitscheduledsubproblemdetermineson/offscheduleofgeneratingunitswhile meetingthesystemandgeneratingunitconstraints.Economicdispatchsubproblem dealswithallocatingloaddemandandspinningreserverequirementsamongthe committedunitsduringeachschedulinghour.
Therefore,mathematically,UChasbeencommonlyformulatedasanonlinear, largescale,mixed-integercombinatorialoptimizationproblemwithconstraintsandthe twosubproblemscanbesolvedseparately.Alargenumberofdeterministicand metaheuristicoptimizationmethodshavebeenappliedtosolvetheUCprobleminthe literature.Deterministicmethodsarefastyetmaymisstheoptimalsolutionand meta-heuristicmethodsare flexibleyetcomputationallyexpensive.However,by combiningthesetwomethods,theycanbenefitfromeachotherandenableustosolve thelargescaleUCproblems.Thus,inthispaper,wehybridizedourproposed Sa-EPSDEwithsimpleprioritylistingmethodtosolvetheUCproblem.Intheproposedhybridsolution,simpleprioritylistingmethodisusedasadeterministicmethod tosolvetheunitscheduledsubproblemandourproposedSa-EPSDEisusedasa metaheuristicmethodtohandletheeconomicdispatchsubproblem.
5.1ProblemFormulation
ObjectiveFunction:Mathematically,overallUCobjectivefunctioncanbedescribedas follows:
MinimizeProductionCost(PC):
where, Fit(Pit) isfuelcostofunit i attime t whichisaquardicfunctionofoutputpower generationofaunit.
ai, bi and ci arecostcoeffi cients.
STit isstartupcostunit i attime t.Thestartupcostdependsonthetimetheunithas beenoffbeforestartup.Thestartupcostwillbecoldstartupcost(SCic )whendown timeduration(Tioff)ofunitiexceedescoldstarthour(c-s-houri)inexcessofminimum downtime(Tidown )andwillbehotstartupcost(SCih )whendowntimedurationisless than c-s-houri inexcessof Tidown.
Constraints:Theconstraintstobesatis fiedduringtheoptimizationareasfollows:
(a)Systempowerbalance:Generatedpowerfromthecommittedunitsmustbebalancedwiththesystempowerloaddemand Dt attime t.
(b)Systemspinningreserverequirement:Spinningreserve Rt isrequiredinthe operationofapowersysteminordertopreventloadinterruptionfromcertain equipmentoutages.Thereserveisusuallyconsideredtobe5%or10%ofthe forecastedloaddemand.
(c)Generationpowerlimits:eachunithasgenerationrangelimitedbytheminimum Pmin i andmaximum Pmax i powervaluesasfollows:
(d)Unitminimumup T up i anddown T down i time:Onceeachunitisturnon/shutdown, itmustbecommitted/decommittedforacertainprede fi nedtimebeforeitisshut down/broughtonline.
(e)Unitinitialstatus:Atthestartoftheschedulingperiod,initialstatusofeachunit mustbetakenintoaccount.
5.2SimplePriorityListingMethod
Prioritylistingmethodisthesimplesolutionwhichisbasedonmaximumpower generationcapacityofeachunit.Theunitswithhighermaximumpowergeneration capacitywillhavehigherprioritytocommit.Thus,aunitwiththehighestmaximum generationpowercapacitywillbelocatedonthetopoftheprioritylistandtheother unitswillbelocatedintheascendingorderoftheirmaximumoutputpowertowardsthe bottomofthelist.Fortheunitsofequalmaximumpowergenerationcapacity,theone withlowerheatratewillhavehigherpriority.Theheatrateiscalculatedaccordingto thefollowingformula:
HRi
Basedontheprioritylist,theunitsarecommitteduntiltheloaddemandandthe spinningreserveconstraintsaresatis fiedateachtimeinterval.
5.3ExperimentalResults
Abenchmarksystemof10generatorsfrom[25]isusedasapowersystemtestbedin thispaper.Theexperimentisconductedfor10-,20-,40-,60-,80-,and100-bus systems.Forthe20-bussystemandabove,thebase10unitsarereplicatedandtheload demandismultipliedaccordingly.Thespinningreserveisconsideredtobe10%ofthe loaddemandinthisexperiment.Theschedulingperiodisforoneday(24h)andthe loaddemandfor24hispresentedinTable 4 [26].Theperformanceofproposed hybridmodelisevaluatedwithotherhybridmodelcombinedbasicDEandPLmethod. Theresultsarecomparedintermsofaverageproductioncostaveragedover30runs
Table4. Loaddemand[26]
Table5. Performancecomparisonintermsofproductioncost($) UnitsystemsSa-EPSDEwithPLDEwithPLCostdifference 10624774.92624807.603.27E+01 201349484.311349520.183.59E+01 402752714.272753588.758.74E+02 604152472.314153835.671.36E+03 805545851.905548220.522.37E+03 1006291795.186293104.691.31E+03
andshowninTable 5.AsseeninTable 5,thehybridmodelofSaEPSDEandPL performsbetterthanDE+PLhybridmodelinallthepowersystems.Especially SaEPSDEwithPLobtainslowerproductioncostof1000*2400perdayonthelarge powersystemof60-,80-and100-bussystems.
6Conclusion
Inthispaper,self-adaptiveDEalgorithmwithensembleofstrategiesandsampled parametervalues(Sa-EPSDE)with F and CR sampledfromthedistributionofdifferent fixedmeanvaluesisproposedtodeterminesuitablestrategywithassociatedparameter settingsfordifferentstagesofthesearchprocess.TheperformanceofSa-EPSDE algorithmistestedonshiftedrotateduni-modalandmulti-modalCEC2005benchmark functionsandcomparedwithSaDEandEPSDEalgorithmson10D,30Dand50D problems.Accordingtomeanandstandarddeviationcriteria,theproposedSa-EPSDE algorithmperformsbetterthanothertwoDEalgorithmsonallthreedimensions.In ordertoevaluatetheperformanceofSa-EPSDEonreal-worldapplications,Sa-EPSDE iscombinedwithprioritylistingmethodandappliedtosolveunitcommitmentpower problem.TheperformanceiscomparedwithbasicDEalgorithmintermsofmean productioncostaveragedover30trialruns.Theexperimentalresultsshowedthat proposedSa-EPSDEofferedlowerproductioncostthancomparedDEalgorithm.
Acknowledgement. ThisworkwassupportedbytheSingaporeNationalResearchFoundation (NRF)underitsCampusforResearchExcellenceandTechnologicalEnterprise(CREATE) programme,andCambridgeAdvancedResearchCentreinEnergyEfficiencyinSingapore (CARES),C4Tproject.
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EmpiricalAssessmentofHumanLearning PrinciplesInspiredPSOAlgorithms onContinuousBlack-BoxOptimizationTestbed
M.R.Tanweer
(B) ,AbdullahAl-Dujaili,andS.Suresh
SchoolofComputerEngineering,NanyangTechnologicalUniversity, Singapore,Singapore {muhammad170,aldujail001}@e.ntu.edu.sg,ssundaram@ntu.edu.sg
Abstract. Thispaperbenchmarkstheperformanceofoneoftherecent researchdirectionsintheperformanceimprovementofparticleswarm optimizationalgorithm;humanlearningprinciplesinspiredPSOvariants.Thisarticlediscussesandprovidesperformancecomparisonofnine differentPSOvariants.TheComparingContinuousOptimizers(COCO) methodologyhasbeenadoptedincomparingthesevariantsonthenoiselessBBOBtestbed,providingusefulinsightregardingtheirrelativeefficiencyandeffectiveness.Thisstudyprovidestheresearchcommunitya comprehensiveaccountofsuitabilityofaPSOvariantinsolvingselective classofproblemsunderdifferentbudgetsettings.Further,certainrectifications/extensionshavealsobeensuggestedfortheselectedPSOvariantsforpossibleperformanceenhancement.Overall,ithasbeenobserved thatSL-PSOandMePSOaremostsuitedforexpensiveandmoderate budgetsettingsrespectively.Further,iSRPSOandTPLPSOhaveprovidedbettersolutionsundercheapbudgetsettingswhereiSRPSOhas shownrobustbehaviour(bettersolutionsoverdimensions).Wehopethis paperwouldmarkamilestoneinassessingthehumanlearningprinciplesinspiredPSOalgorithmsandusedasabaselineforperformance comparison.
Keywords: PSO · HumanlearningprinciplesinspiredPSOvariants · COCOmethodology Black-boxoptimization
1Introduction
ParticleSwarmOptimization(PSO)[10]isapopulation-basedsearchoptimizationalgorithm,inspiredfromthesocialbehavioraswarmofbird.Ithasbeen widelyusedforsolvingnumerousoptimizationproblem[4, 11, 22]andhassuccessfullyprovidedsolutionstothecomplexreal-worldoptimizationproblems [15, 19, 21].Inthepasttwodecades,itssimplicityandcomputationalefficiency hasattractedtheresearchers.Asaresult,severalresearchdirectionshavebeen studiedincludingparametertuning,neighbourhoodtopology,learningstrategies etc.[5, 13, 27].Thecurrentstate-of-the-artresearchinPSOincludesadiverse
c SpringerInternationalPublishingAG2016
B.K.Panigrahietal.(Eds.):SEMCCO2015,LNCS9873,pp.17–28,2016. DOI:10.1007/978-3-319-48959-9 2
collectionofmodifiedPSOvariants,withonevariantperformingbetterthan otheronaclassofoptimizationproblems.Ithasbeenshowninhumanlearningpsychologythathumanbeingsarebetterplannersandpossessintelligent informationprocessingskills.Thishelpsonetoperformbetterself-regulation ofthecognitivestrategiesandhenceenhancethedecisionmakingabilities[14]. Therefore,algorithmsdevelopedusinghumanlearningprincipleshaveshown promisingcharacteristics[17, 20].Inspiredfromthesefindings,researchershave triedtodesignsuchPSOvariantsthatcanprovidebettersolutionsonvarious classesofproblemsbyintroducinghuman-likelearningprinciples[18, 22, 24, 25]. ThisresearchdirectionhasprovidedrobustandefficientPSOvariantscapableofsolvingmoreclassesofoptimizationproblems.Recently,researchershave developedseveralhumanlearningprinciplesinspiredPSOvariantswhichhas significantlyenhancedthealgorithms’performance.Therefore,itisrequiredto benchmarktheperformanceofthesealgorithmstocomeupwithabaselinefor performancecomparisoninassessingthehumanlearningprinciplesinspiredPSO algorithms.
Thispaperaimstowardscomparingeightdifferenthumanlearningprinciples inspiredPSOvariantscarefullyselectedtoreflectdifferentselfandsocialprinciplesappliedtothePSOalgorithmagainstthestandardPSOalgorithm.Through experimentalevaluation,thispapermarksthedifferencesamongthemandinvestigatesthesuitabilityofthealgorithmsforsolvingoptimizationproblemswith differentcharacteristicsfordifferentcomputationalrequirements.Furthermore, itdrawsseveralconcludingpointsthatcanhelpthefuturedevelopmentinthe PSOresearch.Toachievethepaper’sgoals,theexperimentalevaluationmust beabletodiscoveranddistinguishthegoodfeaturesofthevariantsoverothers, andshowtheirdifferencesoverdifferentstagesofthesearchforvariousgoals andsituations.Inthispaper,theComparingContinuousOptimizer(COCO) methodology[7]hasbeenadoptedasitmeetstherequirements.Itcomeswitha testbedof24scalablenoiselessfunctions[8]addressingsuchreal-worlddifficulties asill-conditioning,multi-modality,anddimensionality.TheselectedPSOvariantshavediversebehaviourintermsofsolutionaccuracy,convergencerateand robustness.Somerequiremoretimetoconverge[9, 23]whereasothersarecomputationallyefficient[12, 21, 24].Therefore,theperformancehasbeenassessed overdifferentsettingsoffunctionevaluationstoinvestigatethecapabilityof thealgorithmsinsolvingcomputationallyexpensive,moderateandcheapbudgetoptimizationproblems.Thisevaluationprovidetheresearchersaninsight ofselectionofanappropriatealgorithmdependingonwhatisknownaboutthe optimizationproblemintermsofevaluationbudget,dimensionalityandfunction structure.
Therestofthepaperisorganizedasfollows:Sect. 2 providesabriefdescriptionoftheselectedhumanlearningprinciplesinspiredPSOvariants.InSect. 3, thenumericalassessmentofthealgorithmsispresented,includingexperimentalsetup,procedureforevaluatingthealgorithms’performanceanddiscussion oftheresults.Section 4 summarizesthemainconclusionsfromthisstudy,and suggestspossibleextensionsforfurtherperformanceimprovements.
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“‘The future of medicine’ is a plea for the simplification of medicine, a reaction from the over-elaboration of ‘laboratoryism’ i.e., the instrumental and other laboratory aids to diagnosis. Not that Sir James denies the usefulness of these methods in research work, but he maintains that, while in some conditions it may be necessary even in ordinary clinical work to use elaborate instruments, it should be the constant aim of the medical man to learn how to discard such instrumental aids, and claims that he is now able to do so in much of his clinical work on diseases of the heart. What the author is so strongly opposed to are the laboratory ideals outlined in the syllabus for students recommended by the professor of clinical medicine at the world-famous Johns Hopkins university, Baltimore, reprinted in this book, and occupying more than four closely printed pages. ” Spec
Booklist 16:227 Ap ’20 Sat R 128:466 N 15 ’19 1200w
“One lays aside the book with a feeling of great respect and admiration for this great and honest physician. All the same, one cannot help feeling that the disadvantages of the present system of teaching in the medical schools is exaggerated by the writer, and that, were the attempt made so to alter it as to meet the demands of a man of so keen an intellect as Sir James Mackenzie, a few giants might be reared, but that the work of the average man would suffer.”
Spec 122:476 O 11 ’19 1300w
“The social worker who expects to find in Dr Mackenzie’s book on ‘The future of medicine’ a discussion of the socialization of medicine and the solution of many of the medical problems of the future will be disappointed. The medical and perhaps the lay reader, however, will be amply rewarded by the brilliant and, sometimes, scathing criticism by Dr Mackenzie of the present laboratory research and specialty aspects of medical science.” G: M. Price
Survey 43:438 Ja 17 ’20 240w
“Much thought has been devoted to the composition of this attempt to influence the future of medicine. A good deal of this material is highly technical, which is doubtless unavoidable, but has the disadvantages of making the weighing of the evidence
+ exceedingly difficult for any except members of the medical profession.”
The Times [London] Lit Sup p493 S 18 ’19 1400w
MACKENZIE, JEAN KENYON.[2] Story of a fortunate youth. $1.25 (7c) Atlantic monthly press
These “chapters from the biography of an elderly gentleman” (Subtitle) are sketchy bits from the career of a minister who began life as a little Scotch boy in the East Highlands. His first fortune was a “bawbee” found in the dust, then came real earnings—beginning with six-pence and the duties of a shepherd to help eke out the family income until the great country across the water beckoned him. There the usual course from farm hand and country school-teacher to college and the ministry are gone through, all told lovingly and in whimsical style by the old gentleman’s daughter. The chapters are: The boy and the bawbee; The boy and the half-crown; The boy and the dollar; The wages of youth.
MACKENZIE, JOHN STUART. Arrows of desire; essays on British characteristics. *$3.75 Macmillan 914.2
“The title, borrowed from Blake, and suggesting a romantic novel, is as misleading as Ruskin’s ‘On the construction of sheepfolds.’
Professor Mackenzie’s book consists, in fact, of essays on our [England’s] national character. He discusses ‘Henry V.’ on the assumption that Shakespeare regarded the king as a typical
Englishman. He then considers the English character, taking in turn each of the reproaches hurled at us by native and foreign critics. He contrasts the sister-nations with England, and incidentally repeats what we believe to be the fallacious statement that the Scotsman is more democratic than the Englishman. In the end Professor Mackenzie seems to conclude that we are not so bad after all, and that our chief danger lies in a ‘superficial optimism.’” Spec
“An analysis of British characteristics by a British professor is a difficult task for any fair-minded man, which is probably why Mr J. S. Mackenzie draws upon a consensus of other people’s opinions with which to support his own. This continual reference to authorities is a little wearisome to the flesh, the more so since Mr Mackenzie shows himself a really competent judge of the matter, avoiding selfgratification without the obverse fault of detraction in order to prove himself just.”
Nation 111:19 Jl 3 ’20 350w
“He is too attentive to detail, too eager to back up what he has to say with chapter and verse. The professor in him is uppermost, to the detriment of the writer. Nevertheless, in spite of these handicaps, there is acute analysis in Professor Mackenzie’s book. In its parts his book is good; as a whole it lacks coherence and smoothness.”
N Y Times 25:296 Je 6 ’20 1100w
Reviewed by Archibald MacMechan
Review 2:546 My 22 ’20 1300w Sat R 130:319 O 16 ’20 820w
“It is an entertaining book.” Spec 124:215 F 14 ’20 160w
“With such fair promise it is the more regrettable that we should be compelled, as we are, to admit that the performance is not answerable to the high intent of the author. Not once nor twice, but repeatedly throughout the book, we are confronted with a looseness of thought, a disinclination to get to the heart of his subject which is certainly surprising in an emeritus professor of logic.”
[London] Lit Sup p207 Ap 1 ’20 1950w
KENNETH JAMES JOSEPH.
Cattle and the future of beef-production in England.
*$2.50 (3c) Putnam 636.2
Agr20–243
A British work growing out of the necessity of conserving and increasing the food supply. The author is reader in agriculture in the University of Cambridge, and late editor of the Journal of the Royal Agricultural Society of England, and the preface and one of the chapters are contributed by F. H. A. Marshall, lecturer in agricultural physiology, Cambridge. Contents: Introduction; Store cattle; Grass beef; Winter beef; Beeflings; Dual-purpose cattle; Pedigree breeding; Possibilities of the future; Physiological (by F. H. A. Marshall); Breeds of cattle (four chapters); Index.
“There are many signs that the line of reorganisation which Mr Mackenzie indicates is the one which British agriculture is most likely to follow, and it is sincerely to be hoped that his book will circulate widely amongst the leaders of agricultural opinion and the farming community generally.” C. C.
Nature 105:62 Mr 18 ’20 850w
“Mr Mackenzie’s book is all the more stimulating because he does not profess to deliver a final opinion on any matters.”
Spec 124:278 F 28 ’20
1200w
“Mr Mackenzie is original and daring in some of his suggestions.” The Times [London] Lit Sup p549 O 9 ’19 200w
MACKIE, RANSOM A. Education during adolescence. *$2 Dutton 373
20–4028
“Basing his arguments very largely on Stanley Hall’s ‘Educational problems,’ the author proceeds to describe what are the essentials of a high school curriculum.” (Cleveland) “In the introduction, Dr Hall states that interest is the very Holy Ghost of education and so-called formal studies and methods of discipline are largely a delusion and a snare. They make degenerate mental tissue. In chapter I the author states that the purpose of education, based not only on the needs of
society but also on the needs of the adolescent, are, according to Dr Hall, ‘to train character, to suggest, to awaken, to graft interest, to give range and loftiness of sentiment of view, to broaden knowledge, and to bring everything into touch with life.’ During this age every effort possible should be made to ‘fill and develop mind, heart, soul, and body,’ especially with a view to vocational training. Such training demands vitalized and humanized materials of education and methods of instruction.” (School R)
“A good summary written with forceful simplicity.”
Booklist 17:54 N ’20
Cleveland p91 S ’20 50w
El School J 21:158 O ’20 200w
Reviewed by Paul Shorey
Review 2:433 Ap 24 ’20 1600w
“Taken as a whole, this book is quite suggestive and inspirational. Those persons who find the original works of G. Stanley Hall a little weighty will have their minds refreshed with some of his doctrines by reading Mr Mackie’s book, in which Dr Hall’s philosophy is presented in a very readable style, yet with less tonnage than is found in his own works.” J: B. Clark
School R 28:717 N ’20 820w
MCKIM, WILLIAM DUNCAN. Study for the times. *$2.50 Putnam 150
20–21213
The author calls his study “ an inquiry into thought and motive,” and this he considers imperative in these post-war times of restlessness and impatience, of fads and crazes, of hasty formulation of rights and noisy demand for their concession. Although much in this mad onward rush may be of lasting value and help towards a rejuvenation of the race, the latter, he holds, can only be accomplished through careful patient thought and a study of the limitations and frailties of our own individual natures. The book deals largely with human psychology and the findings of psychopathology. Contents: Introduction; Social influences; The individual mind; The knowing function; The feeling function; Conclusion; Index.
MACKINNON, ALBERT GLENTHORN. Guid auld Jock. *$1.75 (2c) Stokes
19–18839
Jock had a keen relish for other people’s affairs, especially those of Scotchmen. At the military hospital he ferreted out all such and became their father confessor, their lawyer and general confidant. The book is a collection of such confessions, of wrongs committed, of secret sins, of weighted consciences. And every story had its complement. The other man always turned up and in his turn made a confession, and, thanks to Jock’s discretion, quick wit and sense of humor, there was always a righting and a smoothing over. Some of
the titles are: Jock’s neebors; How Jock healed his comrade’s worst wound; The barbed wires of misunderstanding; A prank o ’ the post; A maitter o ’ conscience.
MCKISHNIE, ARCHIE P. Son of courage. il
*$1.75 (2c)
Reilly & Lee
20–17187
Billy Wilson was one of the boys in a small settlement on the north coast of Lake Erie. He was full of fun, always ready for some boyish deviltry and the leader among his chums. The other side of his character was love of nature and animals, undaunted courage and love of fair dealing. He was afraid only of ghosts and even against those he felt secure with his rabbit’s-foot charm. His exploits are many and exasperating but he wins the heart of his stepmother and of the prettiest girl in the settlement and becomes instrumental in solving several mysteries and discovering a treasure.
“A satisfying story of outdoor life.”
Kent Graydon of the Windermere is a young Canadian engineer who has gone West and made good. Since his schoolboy days he has cherished the memory of Alleyne Milburne as his ideal of womanhood. Then one summer he meets her again in his own western country. He woos her ardently and it is not until he loses out to his rival of earlier days that he realizes that it is not she who embodies his ideals, but her cousin Claire, who is “honourable and generous, sportsmanlike and fair, sympathetic and womanly.”
MCLACHLAN, HERBERT. St Luke, the man and his work. *$3 (*7s 6d) Longmans 226
20–14133
“In a dozen chapters, Mr McLachlan, lecturer in Hellenistic Greek in the University of Manchester, discusses St Luke, the man of letters, the linguist, the editor, the theologian, the humorist, the letter writer, the reporter, the diarist, etc. The work gives in brief the views of German and English Protestants and Rationalists on every phase of the Lucan problem authenticity, language, accuracy, doctrine and the like.” Cath World
“This is a book from which the student of the Lucan writers will learn much, whether he is among the conservatives or the revolutionaries in textual criticism.”
Ath p540 Ap 23 ’20 800w
Cath World 111:686 Ag ’20 320w
“This scholarly book is to be commended to the notice of New Testament students.”
+
The Times [London] Lit Sup p111 F 12 ’20 290w
MCLAUGHLIN, ANDREW CUNNINGHAM.[2]
Steps in the development of American democracy. *$1.50 Abingdon press 342.7
20–8377
“A small volume comprising the lectures delivered by Professor McLaughlin at Wesleyan university. This series of lectures was the first to be given on the George Slocum Bennett foundation ‘for the promotion of a better understanding of national problems and of a more perfect realization of the responsibilities of citizenship.’ The author tells us in the preface that his purpose ‘is simply to recount a few salient experiences which helped to make America what it is, ... as also to describe certain basic doctrines and beliefs, some of which may have had their day, while others have not yet reached fulfillment.’” Am Hist R
“In a work of this character, the presentation of new historical facts is not to be expected, but rather a new and fresh treatment of them and of their significance. This latter task is what Mr McLaughlin essayed in this series of lectures and this he has most successfully achieved. Mr McLaughlin’s firm grasp upon the history of the country is apparent throughout his treatment, and his discussion is characterized by brilliant exposition and frequently enlivened by flashes of wit and even restrained sarcasm. ” H. V. Ames
+ + + Am Hist R 26:344 Ja ’21 540w
Am Pol Sci R 14:739 N ’20 50w
“Necessarily, the treatment of the subject is broad but it is marked by a sense of proportion and by genuine insight.”
Bookm 52:368 D ’20 120w
MCLELLAN, ELEANOR. Voice education.
*$1.75 (7½c) Harper 784.9
20–16097
The author claims to have discovered a system of scientific vocal technique through many years of practical research work by beginning with correcting abnormalities of speech and voice action. “This means rectifying conditions such as hoarseness, thickness of the vocal cords and surrounding muscles, nodules, paralyzed vocal cords, loss of high or low notes, stuttering, and all allied phonation and action troubles.” (Preface) The contents are: Breath; Tone versus vowel; Attack and poise of tone; Consonants; Interpretation; Requirements of a great career; Emotions and characteristics of singers.
“Every teacher and singer and just people would do well to take the chapter on ‘Emotions and characteristics of the singer’ in this book to heart. But there the practical help of the book to a singer or teacher ends.”
MACMANUS, SEUMAS. Top o ’ the mornin’.
*$1.90 (3c) Stokes
20–17081
A collection of old and new tales in the Irish dialect. Some of the copyright dates go back to 1899. Others belong to the present year. The titles are: The lord mayor o ’ Buffalo; The Widow Meehan’s Cassimeer shawl; The cadger-boy’s last journey; The minister’s racehorse; The case of Kitty Kildea: Billy Baxter’s holiday; Wee Paidin; When Barney’s trunk comes home; Five minutes a millionaire; Mrs Carney’s sealskin; The capture of Nelly Carribin; The bellman of Carrick; Barney Brian’s monument; All on the brown knowe; The heartbreak of Norah O’Hara.
“Splendid for reading aloud and full of fun and good Irish wit.”
Booklist 17:118 D ’20
“Mr MacManus has a certain delicate whimsicality of utterance that transforms his somewhat sordid characters into beings of real interest. They provide a volume of extremely pleasant little stories, all quite indelibly branded with the mark of the shamrock.”
+ N Y Evening Post p27 O 23 ’20 150w
Boston Transcript p5 N 20 ’20 220w
“Mr MacManus makes potent use of the folk-flavour: he draws his inspiration from the touchstone of common humanity; but he never hesitates to take what liberties he chooses with his material.” L. B.
Freeman 3:238 N 17 ’20 170w
Outlook 126:378 O 27 ’20 60w
Reviewed by H. W. Boynton
Review 3:422 N 3 ’20 380w
Springf’d Republican p8 D 28 ’20 130w
Wis Lib Bul 16:195 N ’20 90w
MCMASTER, JOHN BACH. United States in the world war (1918–1920). v 2 *$3 Appleton 940.373
20–12608
This is the second volume of Professor McMaster’s history of the war. It deals with the work of the American troops in France and ends with the peace conference and the rejection of the peace treaty by the United States senate. Contents: Submarines off our coast; War work at home; Fighting in France; Peace offensives; The armistice; The president goes abroad; The peace conference; The treaty of peace; The treaty rejected; Appendices; Index.
Booklist 17:25 O ’20
“The arrangement may be registered at once as both logical and, within the scope of logic, rhetorical, even dramatic. He did not make as good use as he might have done of the reports of Pershing and March. When the chapter ‘War work at home’ is so well written it is a pity that no attention should be paid to the efforts the enemy was making to render that work futile.” Walter Littlefield
N Y Times p22 Ag 29 ’20 2500w
Outlook 126:202 S 29 ’20 100w
“The second volume is a distinct disappointment. Even considering the haste with which it must have been prepared, the single chapter devoted to the military phase of the war is almost absurdly inadequate and our naval participation is snubbed still more severely. The chapter headed ‘War work at home,’ however, is well done, and the one entitled ‘The treaty rejected,’ considering all the difficulties of the topic, is also handled with considerable skill.”
Review 3:508 N 24 ’20 220w
R of Rs 62:445 O ’20 160w
“We do not observe that Professor McMaster has utilized any sources of information which are not readily accessible; he seems indeed to have relied largely upon the reports in the newspapers. The book is disfigured by some careless mistakes.”
Spec 125:643 N 13 ’20 170w
