Table Of ContentStudies in Systems, Decision and Control 213
Ali Wagdy Mohamed
Diego Oliva
Ponnuthurai Nagaratnam Suganthan Editors
Handbook
of Nature-Inspired
Optimization
Algorithms:
The State of the Art
Volume II: Solving Constrained Single
Objective Real-Parameter Optimization
Problems
Studies in Systems, Decision and Control
Volume 213
SeriesEditor
JanuszKacprzyk,SystemsResearchInstitute,PolishAcademyofSciences,
Warsaw,Poland
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· ·
Ali Wagdy Mohamed Diego Oliva
Ponnuthurai Nagaratnam Suganthan
Editors
Handbook of Nature-Inspired
Optimization Algorithms:
The State of the Art
Volume II: Solving Constrained Single
Objective Real-Parameter Optimization
Problems
Editors
AliWagdyMohamed DiegoOliva
OperationsResearchDepartment DepartmentofComputerSciences
FacultyofGraduateStudiesforStatistical UniversityofGuadalajara
Research Guadalajara,Jalisco,Mexico
CairoUniversity
Giza,Egypt
PonnuthuraiNagaratnamSuganthan
SchoolofEEE
NanyangTechnologicalUniversity
Singapore,Singapore
ISSN 2198-4182 ISSN 2198-4190 (electronic)
StudiesinSystems,DecisionandControl
ISBN 978-3-031-07515-5 ISBN 978-3-031-07516-2 (eBook)
https://doi.org/10.1007/978-3-031-07516-2
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Preface
Generally,optimizationistheprocessoffindingthebestresultsforagivenproblem
undercertainconditions.Inreal-worldproblems,applicationsanddifferentfieldsof
scienceandengineering,mostoftheoptimizationproblemsaresubjecttodifferent
typesofconstraints.Thus,theseproblemsarecalledConstrainedOptimizationProb-
lems (COPs) and are considered challenging and complex due to their variations
in mathematical properties and structures. COPs can be classified in several ways
depending on their mathematical properties. They can be classified based on the
natureofvariables,suchasreal,integeranddiscrete,andmayhaveequalityand/or
inequality constraints. Another important alternative classification depends on the
typeofexpressionofobjectiveandconstraintfunctions.Itcanbeclassifiedaslinearor
non-linear,continuousordiscontinuousandunimodalormultimodal.Furthermore,
thefeasibleregionofsuchproblemscanbeeitheratinyorsignificantportionofthe
searchspace.Itcanbeeitheronesingle-boundedregionoracollectionofmultiple
disjointregions.Duetotheabove-mentioneddifficultycharacteristics,overthepast
decade,manyattemptshavebeenmadetosolveCOPsusingvarioustypesofmeta-
heuristic and evolutionary algorithms together with different constraint-handling
techniques.
This book presents various metaheuristic algorithms on single objective
constrained real-parameter numerical optimization problems. We strongly encour-
aged the authors to test the performance of their proposed state-of-the-art algo-
rithmsoneitheranyreal-worldengineeringapplicationsuchasElectricalandpower
systems,machinelearning,RoboticsandExpertSystems,Patternrecognition,Image
processing, Bioinformatics and bio-medical engineering, Electronics and commu-
nication engineering, and Manufacturing Science or CEC 2017 for constrained
benchmarks.
Chapter“ParticleSwarmOptimizationBasedOptimizationforIndustryInspec-
tion” presents Particle Swarm Optimization (PSO)-based parameter optimization
of the Gabor filter algorithm for the diagnosis of copper defects. The images of
copperstripsundertestareacquired,andtheimageacquisitionsystemoptimizedby
adjustingthelightbrightness and arranging thecamera location. For delamination
defectsdifficultforsegmentation,followedbytheRegionofInterest(ROI)extracted,
v
vi Preface
considerusingtheGaborfiltertoextracttheGaborfeatures,andthenthePSOisused
tooptimizetheparameteroftheGaborfilter;aftertheprocessoftheimprovedimage
contrast,de-noisingandbinarization,thedelaminationdefectscanbedetected.The
experimentalresultshaveprovedthattheproposedPSO-basedparameteroptimiza-
tionoftheGaborfilteralgorithmandimageprocessingcandetectthecopperstrip
defectscorrectly.
Antalgorithmsarestate-of-the-artmetaheuristics,whereanantisusuallyasolu-
tion method that builds a single solution following an iterative process. In each
construction step, the involved ant makes a decision in order to add an element to
the incumbent solution. The decision-making relies on PROB, which is a proba-
bilityfunctionwithtwofeatures:thevisibility(representingtheshort-termprofitof
theinvolvedant)andthetrail(informationobtainedfromotherants)oftheconsid-
eredelementtobeadded.Chapter“AntAlgorithms:FromDrawbackIdentification
toQualityandSpeedImprovement”aimsatshowingthatPROBisnotalwayseffi-
cient with respect to quality and speed. Second, it highlights that instead of using
independent local search procedures for improving the solutions generated by the
ants (as it is often the case in the literature), we can formulate and employ PROB
differently.The followingfields areconsidered toshow howtheantparadigm can
be reconsidered to obtain better results: truck loading, job scheduling and graph
coloring.
The inclusion of targets for reducing carbon emissions by governments at the
global level requires greater deployment of renewable energy in generation plan-
ning, implying further development in electric energy transmission and distri-
bution systems. As a result, there are benefits in the environmental area, but
it imposes greater technological development due to the system’s characteris-
tics related to inertia, stability and more unpredictability. The inclusion of power
invertersindistributedgeneration,microgridsandrenewablegenerationdiffersfrom
conventionalgeneration.Thisshowsgreaterpredictabilitywhenplanningprotection
systems.
Chapter “Fault Location Techniques Based on Traveling Waves with Applica-
tionintheProtectionofDistributionSystemswithRenewableEnergyandParticle
SwarmOptimization”analyzessomefaultdeterminationmethodsbasedontraveling
waves in distribution systems. Due to the wide and diverse structure implemented
byelectricenergydistributionsystemsbylargenumbersoffeedersandsubfeeders,
several researchers use multiple techniques to employ methods based on traveling
wavesintheelectricenergydistributionsystem.Thedevelopmentofthetraveling-
wavefaultlocatorbasedonIEC61,850hasalsoarousedearlyinspirationfromthe
researchcommunity asitwasdebated indetailanditsresultsenforced,leadingto
new requests for scientific assessments on the modernization of power grids elec-
trical.Theoptimizationoftheparticleswarmdefinedbytheoriginalmethodology
withbasicBPSOappliedtodeterminethefaultlocationoftheelectricitydistribution
networkcontainsadistinctionconstraint,whichwhenimproved,demonstratesthe
robustness,precision,speedandflexibilityofthemethod:themethodimprovedhas
the ability to find all fault sections quickly on the occasion of single section and
multiplesectionfault.
Preface vii
In chapter “Improved Particle Swarm Optimization and Non-quadratic Penalty
Method for Non-linear Programming Problems with Equality Constraints”, a new
typeofalgorithmisusedtosolvetheNon-LinearProgrammingProblems(NLPP)
with equality constraints and discusses the performance measure. The NLPP with
equality constraint is solved by a special type of penalty function, which is non-
quadraticinnature.Further,thesameNLPPissolvedbytheconventionalquadratic
penaltymethod.AnimprovedversionofthefamousmetaheuristicParticleSwarm
Optimization(PSO)isusedforthesolutionpurpose.
Metaheuristicoptimizationoverthelastdecadehasbeenprogressedremarkably
andisbeingutilizedindifferentfieldswiththeaimofimprovingtheperformances
andachievednotablesuccesses.Followingtheefficacyoftheoptimizationusagein
different domains, it was later also adopted in biometric systems to improve their
accuracyandrobustness.Chapter“RecentTrendsinFaceRecognitionUsingMeta-
heuristic Optimization” presents an unprecedented overview of the usage of opti-
mization techniques in face recognition systems. Various works presented in the
literaturehavebeendiscussed,whichhaveemployedtheoptimizationtechniquesin
differentstepsoffacerecognitionsystems.Then,systematicanalysesandfindings
aboutdifferentoptimizationframeworksforfacebiometricsareintroduced.
Metaheuristic algorithms are intelligence optimization techniques that showed
satisfactoryperformanceindealingwithreal-worldconstrainedoptimizationprob-
lems. One of the most recent metaheuristic methods is Chaos Game Optimization
(CGO), which has high ability in solving various types of optimization problems.
Chapter“ChaosGameOptimizationAlgorithmwithCrossoverOperatorforSolving
Constraint Engineering Optimization Problems” introduces the Crossover based
Chaos Game Optimization (CrCGO) algorithm by embedding the crossover oper-
atorinthesearchprocessoftheCGOalgorithm.Atotalnumberof30benchmark
constrained engineering optimization problems are used to study the capability of
CrCGOindealingwithchallengingoptimizationproblems.
Unmannedaerialvehicles(UAVs)provideanenergy-efficientandrobustsolution
fordatacollection fromtheInternetofThings (IoT)devices. However, theUAV’s
deploymentoptimization,includinglocationsoftheUAV’sstoppoints,isnecessary
tosavetheoverallenergyconsumptionandconductthedatacollectionefficiently.
Chapter “UAV-Assisted IoT Data Collection Optimization Using Gaining-Sharing
Knowledge Algorithm” proposes the gaining-sharing knowledge (GSK) algorithm
foroptimizingtheUAV’sdeployment.
Harvesting the energy from environmental sources is a promising solution for
the perpetual and continuous operation of biomedical wearable devices. Although
the energy harvesting technology ensures the availability of energy source, power
management is crucial to ensure the prolonged and stable operation under a strin-
gentpowerbudget.Chapter“EnergyAwareTikhonov-RegularizedFPATechnique
forTaskSchedulinginWearableBiomedicalDevices”proposesanovelbiosensor
task scheduling of energy harvesting-based biomedical wearable devices for opti-
mizing energy consumption, and hence maximizing the voltage across the energy
storageelement.TheproposedapproachisbasedonFlowerPollinationAlgorithm
(FPA).
viii Preface
Chapter “Material Generation Algorithm Combined with Epsilon Constraint
Handling Scheme for Engineering Optimization” implements a new constraint
handling methodology to the Material Generation Algorithm (MGA) as a recently
developed metaheuristicalgorithm.Theadvanced principlesofmaterialchemistry
regardingtheconfigurationofchemicalreactionsandchemicalcompoundsingener-
atingnovelmaterialsareinperspective.Theepsilonconstrainthandlingapproachas
awell-knownhandlingschemeisappliedtoMGA,whichhasbeeninvestigatedfor
thefirsttimeasahandlingapproachforthisalgorithm.Fornumericalinvestigations,
8ofthewell-knownengineeringdesignproblemswhichhavebeenbenchmarkedby
theCECareutilized.
In recent decades, due to the limitations of resources and equipment in the
constructionofengineeringprojects,theoptimaluseofexistingfacilitiesforreducing
theoverallcostsoftheprojectshasbeenofgreatimportance.Inchapter“Optimum
DesignofTrussStructureswithAtomicOrbitalSearchConsideringDiscreteDesign
Variables”, the optimum design of truss structures is considered utilizing Atomic
OrbitalSearch(AOS)asoneoftherecentlydevelopedmetaheuristicalgorithms.The
applicabilityofthistechniqueintheweightoptimizationofcomplextrussstructures
isinperspective.
Itis worthpointing outthat thedevelopments in nature-inspired computing are
so rapid that it is estimated that there are more than 150 algorithms and variants
inthecurrent literature.Thus, itisnotpossible and notour intention toreview all
of them. Instead, we have focused on the diversity and different characteristics of
algorithmicstructuresandtheircapabilitiesinsolvingawiderrangeofproblemsin
variousdisciplines.
Giza,Egypt AliWagdyMohamed
Guadalajara,Mexico DiegoOliva
Singapore,Singapore PonnuthuraiNagaratnamSuganthan
July,2022
Contents
ParticleSwarmOptimizationBasedOptimizationforIndustry
Inspection ........................................................ 1
HaoWu
Ant Algorithms: From Drawback Identification to Quality
andSpeedImprovement ........................................... 17
NicolasZufferey
Fault Location Techniques Based on Traveling Waves
with Application in the Protection of Distribution Systems
withRenewableEnergyandParticleSwarmOptimization ............ 29
VicenteTiburciodosSantosJúnior
Improved Particle Swarm Optimization and Non-quadratic
Penalty Method for Non-linear Programming Problems
withEqualityConstraints .......................................... 59
RajuPrajapati,OmPrakashDubey,andEfrenMezura-Montes
Recent Trends in Face Recognition Using Metaheuristic
Optimization ...................................................... 85
NourElhoudaChalabi,AbdelouahabAttia,AbderraoufBouziane,
MahmoudHassaballah,andZahidAkhtar
ChaosGameOptimizationAlgorithmwithCrossoverOperator
forSolvingConstraintEngineeringOptimizationProblems ........... 113
SiamakTalatahariandHadiBayzidi
UAV-Assisted IoT Data Collection Optimization Using
Gaining-SharingKnowledgeAlgorithm ............................. 135
RaniaM.Tawfik, HazemA.A.Nomer, M.SaeedDarweesh,
AliWagdyMohamed,andHassanMostafa
ix
