Handbook of Metaheuristics PDF
Preview Handbook of Metaheuristics
International Series in Operations Research & Management Science Volume 146 SeriesEditor: FrederickS.Hillier StanfordUniversity,CA,USA SpecialEditorialConsultant: CamilleC.Price StephenF.AustinStateUniversity,TX,USA Forfurthervolumes: http://www.springer.com/series/6161 · Michel Gendreau Jean-Yves Potvin Editors Handbook of Metaheuristics Second Edition 123 Editors MichelGendreau Jean-YvesPotvin De´partementdemathe´matiquesetde De´partementd’informatiqueetde ge´nieindustriel rechercheope´rationnelle E´colePolytechniquedeMontre´al,and Universite´deMontre´al,andCentre Centreinteruniversitairederecherche interuniversitairederecherche surlesre´seauxd’entreprise surlesre´seauxd’entreprise lalogistiqueetletransport lalogistiqueetletransport C.P.6079,succ.Centre-ville C.P.6128,succ.Centre-ville Montre´al,QCH3C3A7,Canada Montre´al,QCH3C3J7,Canada [email protected] [email protected] ISBN978-1-4419-1663-1 e-ISBN978-1-4419-1665-5 DOI10.1007/978-1-4419-1665-5 SpringerNewYorkDordrechtHeidelbergLondon LibraryofCongressControlNumber:2010933095 (cid:2)c SpringerScience+BusinessMedia,LLC2003,2010 Allrightsreserved.Thisworkmaynotbetranslatedorcopiedinwholeorinpartwithoutthewritten permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY10013,USA),exceptforbriefexcerptsinconnectionwithreviewsorscholarlyanalysis.Usein connection with any form of information storage and retrieval, electronic adaptation, computer software,orbysimilarordissimilarmethodologynowknownorhereafterdevelopedisforbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not theyaresubjecttoproprietaryrights. Printedonacid-freepaper SpringerispartofSpringerScience+BusinessMedia(www.springer.com) A` nose´pousesJohanneetLynneeta` nos enfantsCatherine,Laurent,Gabrielle, Ste´phanieetSimon. Preface The first edition of the Handbook of Metaheuristics was published in 2003 under theeditorshipofFredGloverandGaryA.Kochenberger. Giventhenumerousde- velopmentsobservedinthefieldofmetaheuristicsinrecentyears,itappearedthat thetimewasripeforasecondeditionoftheHandbook.Fordifferentreasons,Fred and Gary were unable to accept Springer’s invitation to prepare this second edi- tionandtheysuggestedthatweshouldtakeovertheeditorshipresponsibilityofthe Handbook.Wearedeeplyhonoredandgratefulfortheirtrust. As stated in the first edition, metaheuristics are “solution methods that orches- trateaninteractionbetweenlocalimprovementproceduresandhigherlevelstrate- gies to create a process capable of escaping from local optima and performing a robust search of a solution space.” Although this broad characterization still holds today,manynewandexcitingdevelopmentsandextensionshavebeenobservedin the last few years. We think in particular to hybrids, which take advantage of the strengthsofeachoftheirindividualmetaheuristiccomponentstobetterexplorethe solution space. Hybrids of metaheuristics with other optimization techniques, like branch-and-bound,mathematicalprogrammingorconstraintprogrammingarealso increasingly popular. On the front of applications, metaheuristics are now used to findhigh-qualitysolutionstoanever-growingnumberofcomplex,ill-definedreal- worldproblems,inparticularcombinatorialones. ThissecondeditionoftheHandbookofMetaheuristics,throughits21chapters, isdesignedtoprovideabroadcoverageoftheconcepts,implementations,andap- plicationsinthisimportantfieldofoptimization.Weweregladtogetapositivere- sponsefromrenownedexpertsforeachchapter.Theyeitheracceptedtoreviseand update their chapter from the first edition or to write brand new ones. The Hand- book now includes updated chapters on the best known metaheuristics, including simulatedannealing,tabusearch,variableneighborhoodsearch,scattersearchand path relinking, genetic algorithms, memetic algorithms, genetic programming, ant colonyoptimization,multi-startmethods,greedyrandomizedadaptivesearchproce- dure,guidedlocalsearch,hyper-heuristics,andparallelmetaheuristics.Italsocon- tains three new chapters on large neighborhood search, artificial immune systems, andhybridmetaheuristics.Thelastfourchaptersaredevotedtomoregeneralissues vii viii Preface relatedtothefieldofmetaheuristics,namelyreactivesearch,stochasticsearch,fit- nesslandscapeanalysis,andperformancecomparison.Afewchaptersfromthefirst editionwerediscarded,astheyappeartobelessrelevant. WethinkthatthisHandbook willbeagreatreferenceforresearchersandgrad- uate students, as well as practitioners. Each presentation, although exhibiting in- evitable stylistic differences, adheres to some common principles which results in stand-alonechaptersthatcanbereadindividually. We are grateful to all authors for taking the time to write the chapters that ap- pear in this Handbook. We are also very grateful to Fred Hillier, Neil Levine, and MatthewAmboyofSpringerfortheirencouragements,support,andpatienceatthe differentstagesofproductionofthisbook. Montreal,Canada MichelGendreau March2010 Jean-YvesPotvin Preface to First Edition Metaheuristics,intheiroriginaldefinition,aresolutionmethodsthatorchestratean interactionbetweenlocalimprovementproceduresandhigherlevelstrategiestocre- ateaprocesscapableofescapingfromlocaloptimaandperformingarobustsearch ofasolutionspace.Overtime,thesemethodshavealsocometoincludeanyproce- duresthatemploystrategiesforovercomingthetrapoflocaloptimalityincomplex solutionspaces,especiallythoseproceduresthatutilizeoneormoreneighborhood structuresasameansofdefiningadmissiblemovestotransitionfromonesolution toanother,ortobuildordestroysolutionsinconstructiveanddestructiveprocesses. The degree to which neighborhoods are exploited varies according to the type of procedure. In the case of certain population-based procedures, such as genetic algorithms, neighborhoods are implicitly (and somewhat restrictively) defined by reference to replacing components of one solution with those of another, by vari- ously chosen rules of exchange popularly given the name of “crossover.” In other population-based methods, based on the notion of path relinking, neighborhood structures are used in their full generality, including constructive and destructive neighborhoodsaswellasthosefortransitioningbetween(complete)solutions.Cer- tainhybridsofclassicalevolutionaryapproaches,whichlinkthemwithlocalsearch, also use neighborhood structures more fully, though apart from the combination process itself. Meanwhile, “single thread” solution approaches, which do not un- dertaketomanipulatemultiplesolutionssimultaneously,runawidegamutthatnot only manipulate diverse neighborhoods but incorporate numerous forms of strate- giesrangingfromthoroughlyrandomizedtothoroughlydeterministic,dependingon theelementssuchasthephaseofsearchor(inthecaseofmemory-basedmethods) thehistoryofthesolutionprocess.1 1Methodsbasedonincorporatingcollectionsofmemory-basedstrategies,invokingformsofmem- orymoreflexibleandvariedthanthoseusedinapproachessuchastreesearchandbranchand bound,aresometimesgroupedunderthenameAdaptiveMemoryProgramming.Thisterm,which originatedinthetabusearchliteraturewheresuchadaptivememorystrategieswerefirstintroduced andcontinuetobetheprimaryfocus,isalsosometimesusedtoencompassothermethodsthathave morerecentlyadoptedmemory-basedelements. ix
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