Handbook of Metaheuristics

669 Pages·2010·5.5 MB·English

by  Alexander G. Nikolaev

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
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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
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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.
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