Cultural Algorithms: Recent Advances

277 Pages·2023·9.184 MB·English

by  Shahin Jalili

Engineering Optimization: Methods and Applications
Shahin Jalili
Cultural Algorithms
Recent Advances
Engineering Optimization: Methods
and Applications
SeriesEditors
AnandJ.Kulkarni,DepartmentofMechanicalEngineering,SymbiosisInstitute
ofTechnology,Pune,Maharashtra,India
AmirH.Gandomi,Engineering&InformationTechnology,University
ofTechnologySydney,Sydney,NSW,Australia
SeyedaliMirjalili,Brisbane,QLD,Australia
NikosD.Lagaros,NationalTechnicalUniversityofAthens,Athens,Greece
WarrenLiao,LSU,ConstructionManagementDepartment,BatonRouge,LA,
USA
Optimizationcarriesgreatsignificanceinbothhumanaffairsandthelawsofnature.
It refers to a positive and intrinsically human concept of minimization or maxi-
mization to achieve the best or most favorable outcome from a given situation.
Besides, as the resources are becoming scarce there is a need to develop methods
andtechniqueswhichwillmakethesystemsextractmaximumfromminimumuse
of these resources, i.e. maximum utilization of available resources with minimum
investment or cost of any kind. The resources could be any, such as land, mate-
rials, machines, personnel, skills, time, etc. The disciplines such as mechanical,
civil, electrical, chemical, computer engineering as well as the interdisciplinary
streamssuchasautomobile,structural,biomedical,industrial,environmentalengi-
neering,etc.involveinapplyingscientificapproachesandtechniquesindesigning
anddevelopingefficientsystemstogettheoptimumanddesiredoutput.Themulti-
facetedprocessesinvolvedaredesigning,manufacturing,operations,inspectionand
testing, forecasting, scheduling, costing, networking, reliability enhancement, etc.
Thereareseveraldeterministicandapproximation-basedoptimizationmethodsthat
have been developed by the researchers, such as branch-and-bound techniques,
simplex methods, approximation and Artificial Intelligence-based methods such
as evolutionary methods, Swarm-based methods, physics-based methods, socio-
inspired methods, etc. The associated examples are Genetic Algorithms, Differen-
tial Evolution, Ant Colony Optimization, Particle Swarm Optimization, Artificial
BeeColony,GreyWolfOptimizer,PoliticalOptimizer,CohortIntelligence,League
ChampionshipAlgorithm,etc.Thesetechniqueshavecertainadvantagesandlimi-
tationsandtheirperformancesignificantlyvarieswhendealingwithacertainclass
of problems including continuous, discrete, and combinatorial domains, hard and
soft constrained problems, problems with static and dynamic in nature, optimal
control,anddifferenttypesoflinearandnonlinearproblems,etc.Thereareseveral
problem-specificheuristicmethodsarealsoexistingintheliterature.
This series aims to provide a platform for a broad discussion on the devel-
opment of novel optimization methods, modifications over the existing methods
including hybridization of the existing methods as well as applying existing opti-
mization methods for solving a variety of problems from engineering streams.
This series publishes authored and edited books, monographs, and textbooks. The
series will serve as an authoritative source for a broad audience of individuals
involvedinresearchandproductdevelopmentandwillbeofvaluetoresearchersand
advancedundergraduateandgraduatestudentsinengineeringoptimizationmethods
andassociatedapplications.
Shahin Jalili
Cultural Algorithms
Recent Advances
ShahinJalili
SchoolofEngineering
UniversityofAberdeen
Aberdeen,UK
ISSN 2731-4049 ISSN 2731-4057 (electronic)
EngineeringOptimization:MethodsandApplications
ISBN 978-981-19-4632-5 ISBN 978-981-19-4633-2 (eBook)
https://doi.org/10.1007/978-981-19-4633-2
©TheEditor(s)(ifapplicable)andTheAuthor(s),underexclusivelicensetoSpringerNature
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Preface
According to the biocultural evolutionary theory, genes and culture are two inter-
actingformsofinheritanceandoverallhumanevolutioncanbeviewedastheproduct
of the changes in biological and cultural traits. Both genetic and cultural evolu-
tionaryprocessesincludeaformofinformationtransmission.Ingeneticevolution,
the genetic information embedded within the genes is being vertically transmitted
fromparentstooffspring.Inculturalevolution,theculturalvariantssuchasbeliefs,
habits,skills,traditions,andpreferences,passfromonegenerationtothenextand
theycanbechangedorevenreplacedbynewonesovertimeduetochangesinthe
culturalenvironment.Incomparisontogeneticevolution,theinformationtransmis-
sioninculturalevolutionisamuchmorecomplicatedprocess.Theinformationflow
inculturalevolutionincludesbothverticalandhorizontaltransmissionsinwhichthe
cultural information is not only vertically inherited from parents to offspring, but
also offspring have the opportunity to socially learn and acquire information from
othermembersofthesociety.
Culturalalgorithms(CAs)aremeta-heuristicnumericaloptimisationalgorithms
inspired by the abovementioned biocultural evolutionary theory. CAs have some
characteristic features that make them unique in comparison to other evolutionary
algorithms (EAs). They model the biocultural evolutionary theory to perform the
search process for global optima, in which both types of vertical and horizontal
learningbehavioursofindividualsaremodelled.Sincetheiremergence,CAshave
been extended and successfully employed to solve a wide variety of problems in
differentbranchesofscienceandtechnology.
The main aim of this book is to explore the recent advances in the algorithmic
frameworkofCAsandtheirapplicationstothedifferentproblemsintheliterature.
Whilethemainapproachofthebookistobrieflydiscussandexplaintheapplication
studiesandalgorithmicdetailsofCAs,thedetailedmathematicalformulationsand
algorithmicpseudo-codesarealsodiscussedineachchaptertoprovideaclearexpla-
nationsforthedifferentconcepts.Thebookismainlyaimedatpostgraduatestudents
andresearchersincomputerscienceandengineeringsubjectswithresearchinterests
inoptimisationandmeta-heuristicalgorithms.Throughoutthebook,itisassumed
v
vi Preface
thatthereadersarefamiliarwiththebasicconceptsofoptimisationtheoryandmeta-
heuristicalgorithms.However,theauthortriedtoproviderelevantreferencesineach
chaptertoassistthereadersinunderstandingthecontentsofthebook.
Thebookcomprisesninedifferentchaptersdividedintothreeparts.PartIcontains
the basic concepts of standard CAs and their theoretical background. The first
part explains how the basic concepts in biocultural evolutionary theory have been
employedtodevelopstandardCAs.PartIIdiscussestheapplicationsofCAstoawide
rangeofreal-worldproblemsandpresentstheirdetailedmathematicalformulations,
includingdecisionvariables,objectivefunctions,andconstraints.PartIIIinvestigates
thedifferentvariantsofCAsdevelopedinliteratureandtheiralgorithmicdetails.The
third part includes a comprehensive survey and detailed pseudo-codes of different
extended, hybrid, and multi-population versions of CAs. The last chapter of the
bookpresentstheapplicationstudyofCAstothereal-worldstructuraloptimisation
problems.
Althoughasignificantefforthasbeenmadetominimisetheerrorsandtyposinthe
book,theauthorwarmlywelcomesreceivingfeedback,suggestions,andcomments
fromreadersonthecontentsofthebook.
The author would like to thank the series editors, Prof. Amir H. Gandomi and
Dr.AnandJ.Kulkarni,aswellasthepublishingeditor,Ms.KamiyaKhatter,andher
colleagues in Springer Nature for their efforts and supports during the production
processofthisbook.
Aberdeen,Scotland,UK ShahinJalili
April2022
Contents
PartI Foundations
1 IntroductiontoStochasticOptimisation .......................... 3
1.1 Introduction ............................................... 3
1.2 ConventionalOptimisationMethods .......................... 4
1.3 ModernStochasticMethods .................................. 6
1.3.1 Heuristics .......................................... 6
1.3.2 Meta-Heuristics .................................... 8
1.3.3 NoFreeLunchTheorems ............................ 8
1.3.4 EvolutionaryAlgorithms(EAs) ....................... 9
1.3.5 SwarmIntelligence(SI) .............................. 11
1.3.6 Socio-inspiredAlgorithms ........................... 11
1.3.7 PhysicsInspiredAlgorithms .......................... 12
1.3.8 Hyper-Heuristics .................................... 13
1.4 Summary .................................................. 13
References ..................................................... 14
2 IntroductiontoCulture ......................................... 17
2.1 Introduction ............................................... 17
2.2 WhatistheCulture? ........................................ 19
2.3 CharacteristicFeaturesofCulture ............................. 20
2.4 CulturalLearninginHumansandAnimals ..................... 21
2.5 CulturalEvolution .......................................... 21
2.6 SocialLearningBiases ...................................... 23
2.7 WhoCanChangeCulturalVariants? .......................... 25
2.8 Summary .................................................. 26
References ..................................................... 27
3 CulturalAlgorithms(CAs) ...................................... 29
3.1 Introduction ............................................... 29
3.2 OverallFramework ......................................... 30
3.3 PopulationSpace ........................................... 32
vii
viii Contents
3.4 BeliefSpace ............................................... 33
3.4.1 SituationalKnowledge ............................... 33
3.4.2 NormativeKnowledge ............................... 37
3.4.3 HistoryKnowledge ................................. 38
3.4.4 TopographicalKnowledge ............................ 39
3.4.5 DomainKnowledge ................................. 40
3.5 CommunicationProtocols ................................... 40
3.5.1 AcceptanceFunction ................................ 41
3.5.2 InfluenceFunction .................................. 41
3.6 CulturalAdjustment ........................................ 44
3.7 AlgorithmicSteps .......................................... 45
3.8 IllustrativeExample ........................................ 46
3.9 Summary .................................................. 51
References ..................................................... 52
PartII ApplicationofCulturalAlgorithms
4 ApplicationsofCulturalAlgorithmsinEngineering ............... 61
4.1 Introduction ............................................... 61
4.2 CivilEngineering ........................................... 62
4.2.1 TransportationEngineering ........................... 63
4.2.2 StructuralEngineering ............................... 65
4.2.3 EnvironmentalEngineering ........................... 66
4.2.4 UrbanEngineering .................................. 67
4.2.5 WaterEngineering .................................. 68
4.3 MechanicalEngineering ..................................... 70
4.3.1 DamageDetection .................................. 70
4.3.2 OptimumDesign ................................... 73
4.3.3 CompositeStructures ................................ 76
4.3.4 OptimalVibrationControl ........................... 78
4.3.5 VehicleSafety ...................................... 79
4.3.6 FlightControl ...................................... 79
4.3.7 GasTurbineOperation ............................... 80
4.3.8 RenewableEnergy .................................. 80
4.4 ChemicalEngineering ....................................... 81
4.4.1 PenicillinProduction ................................ 82
4.4.2 AmmoniaSynthesis ................................. 83
4.4.3 DieselBlending .................................... 84
4.4.4 WaxPrecipitation ................................... 84
4.5 ElectricalEngineering ....................................... 85
4.5.1 Economic/Environmental Dispatch of Power
Systems ........................................... 85
4.5.2 PowerDistributionSystems .......................... 88
4.5.3 PhasedArrayRadars ................................ 89
4.5.4 WirelessSensorNetworks ............................ 89
Contents ix
4.5.5 SignalProcessing ................................... 91
4.5.6 MaintenanceScheduling ............................. 92
4.5.7 DigitalWatermarking ................................ 93
4.5.8 PowerSystemStabiliserDesign ....................... 93
4.5.9 OptimalReactivePowerDispatch ..................... 94
4.5.10 OptimalOperationofCascadeHydropowerStation ...... 95
4.5.11 OptimalDesignofPassivePowerFilters ............... 96
4.5.12 HydrothermalScheduling ............................ 97
4.5.13 HydroproductionScheduling ......................... 99
4.5.14 Multi-robotTasksAllocation ......................... 100
4.6 IndustrialEngineering ....................................... 100
4.6.1 SupplyChainManagement ........................... 101
4.6.2 IndustrialAutomation ............................... 102
4.7 Summary .................................................. 102
References ..................................................... 102
5 ApplicationsofCulturalAlgorithmsinDifferentBranches
ofScience ...................................................... 111
5.1 Introduction ............................................... 111
5.2 ComputerScience .......................................... 112
5.2.1 FunctionOptimisation ............................... 112
5.2.2 Job-ShopScheduling ................................ 115
5.2.3 FacilityLocation .................................... 116
5.2.4 SetCovering ....................................... 118
5.2.5 SemanticNetwork .................................. 119
5.2.6 ProgramUnderstanding .............................. 120
5.2.7 Timetabling ........................................ 120
5.2.8 IntelligentLogistics ................................. 120
5.2.9 ArtificialNeuralNetworks ........................... 121
5.2.10 Multi-aircraftTargetAllocation ....................... 121
5.2.11 TravellingSalesmanProblem ......................... 123
5.2.12 DataMining ....................................... 123
5.2.13 CloudComputing ................................... 124
5.2.14 FogComputing ..................................... 127
5.2.15 OnlineMarketing ................................... 128
5.2.16 GeneticProgramming ............................... 128
5.2.17 Games ............................................ 128
5.2.18 SoftwareTesting .................................... 129
5.2.19 ImageSegmentation ................................. 129
5.2.20 ImageMatching .................................... 130
5.2.21 SonarSystems ...................................... 130
5.2.22 GestureRecognition ................................. 130
5.3 SocialScience ............................................. 131
5.3.1 Anthropology ...................................... 131
5.3.2 VirtualEnterprise ................................... 131