Biologically-Inspired Optimisation Methods: Parallel Algorithms, Systems and Applications

364 Pages·2009·11.602 MB·English

by  Kalyanmoy Deb (auth.), Andrew Lewis, Sanaz Mostaghim, Marcus Randall (eds.)

AndrewLewis,SanazMostaghim,andMarcusRandall(Eds.)
Biologically-InspiredOptimisationMethods
StudiesinComputationalIntelligence,Volume210
Editor-in-Chief
Prof.JanuszKacprzyk
SystemsResearchInstitute
PolishAcademyofSciences
ul.Newelska6
01-447Warsaw
Poland
E-mail:[e-mail]
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NewAdvancesinIntelligentDecisionTechnologies,2009 Biologically-InspiredOptimisationMethods,2009
ISBN978-3-642-00908-2 ISBN978-3-642-01261-7
Andrew Lewis,SanazMostaghim,
and Marcus Randall (Eds.)
Biologically-Inspired
Optimisation Methods
Parallel Algorithms,Systems and Applications
123
Dr.AndrewLewis Assoc.Prof.MarcusRandall
InstituteforIntegratedandIntelligentSystems FacultyofBusiness
GriffithUniversity TechnologyandSustainableDevelopment
NathanCampus BondUniversity
Brisbane,Queensland,4111 GoldCoast,Queensland,4229
Australia Australia
Email:a.lewis@griffith.edu.au Email:[e-mail]
Dr.-Ing.SanazMostaghim
InstitutfürAngewandteInformatikund
FormaleBeschreibungsverfahren-AIFB
UniversitätKarlsruhe
76128Karlsruhe
Germany
Email:[e-mail]
ISBN 978-3-642-01261-7 e-ISBN978-3-642-01262-4
DOI 10.1007/978-3-642-01262-4
Studiesin Computational Intelligence ISSN1860949X
Library of Congress Control Number:Applied for
(cid:1)c 2009 Springer-VerlagBerlin Heidelberg
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Preface
Throughout the evolutionary history of this planet, biological systems have
been able to adapt, survive and flourish despite the turmoils and upheavals
of the environment. This ability has long fascinated and inspired people to
emulate and adapt natural processes for application in the artificial world
of human endeavours. The realm of optimisation problems is no exception.
In fact, in recent years biological systems have been the inspiration of the
majority of meta-heuristic search algorithms including, but not limited to,
geneticalgorithms,particleswarmoptimisation,antcolonyoptimisationand
extremal optimisation.
Thisbookpresentsacontinuumofbiologicallyinspiredoptimisation,from
the theoretical to the practical. We begin with an overview of the field
of biologically-inspired optimisation, progress to presentation of theoretical
analysesandrecentextensionstoavarietyofmeta-heuristicsandfinallyshow
applicationtoanumberofreal-worldproblems.Assuch,itisanticipatedthe
book will provide a useful resource for reseachers and practitioners involved
in any aspect of optimisation problems.
The overviewofthefieldisprovidedbytwoworksco-authoredbyseminal
thinkers in the field. Deb’s “Evolution’s Niche in Multi-Criterion Problem
Solving”, presents a very comprehensive and complete overview of almost
all major issues in Evolutionary Multi-objective Optimisation (EMO). This
chapter starts with the original motivation for developing EMO algorithms
andprovidesanaccountofsomesuccessfulproblemdomainsonwhichEMO
has demonstrated a clear edge over their classical counterparts.
JaimesandCoelloCoello’s“ApplicationsofParallelPlatformsandModels
inEvolutionaryMulti-ObjectiveOptimization”presentsanoverviewofstate-
of-the-art systems that exploit coarse and fine grained parallelism to solve
multi-objective optimisation problems. Standard parallelisation models are
reviewedin the contextof multi-objectiveoptimisation,and methods for the
detailedassessmentoftheirperformancearediscussed.Inaddition,anumber
ofnovelschemesforparallelisationofmulti-objectiveevolutionaryalgorithms
VI Preface
arebrieflyreviewed.Thediscussionalsoincludescommentonhowtheglobal
phenomenon of meta-computing can be used to solve these problems.
Globalmeta-computingbringsanewsetofproblemstobeovercomeinthe
implementation of optimisation algorithms. In particular, the heterogeneous
and dynamic nature of the computing environment require that greater con-
sideration be given to the fault tolerance of algorithms. Lewis, Mostaghim
andScrivenbegintoconsidertheseissuesin“AsynchronousMulti-Objective
OptimisationinUnreliableDistributedEnvironments”,analysingtheperfor-
mance of multi-objective particle swarm optimisation (MOPSO) algorithms
in unreliable computing environments, giving a detailed consideration of a
novelapproachofasynchronousupdates in parallelMOPSOalgorithms that
significantly improves fault tolerance, and suggesting a variety of methods
for adapting algorithms to “churn” of computing resources.
The consideration of recent and emerging developments of metaheuristics
is continued by an exploration of dynamic optimisation problems, a class of
optimisation problems that have many real-world characteristics, yet have
received relatively little attention. These are difficult problems that change
their structure and/or problem data while the meta-heuristic attempts to
solve the problem. A comprehensive survey of genetic algorithms, particle
swarm optimisation, ant colony optimisation and extremal optimisation ap-
proachesandimplementationsispresentedinHendtlass,MoserandRandall’s
“Dynamic Problems and Nature Inspired Meta-heuristics”. For each of the
methodsdiscussed,considerationisgiventopracticalissuesofapplicationto
a variety of benchmark and real-worldproblems.
Artificial neural networks are another group of biologically inspired tech-
niques,wellsuitedtopatternrecognitiontasks.“RelaxedLabellingusingDis-
tributedNeuralNetworks”byJimAustinexploresaformofneuralnetworks,
knownascorrelationmatrixmemories,andtheiruseinimplementingthe re-
laxationlabellingtechniquefordealingwithconstraintsatisfactionproblems,
inparticulargraphmatching.ThemethodsarebuiltintheAdvancedUncer-
tain Reasoning Architecture (AURA), a tool framework made available free
on the Internet. An interesting application demonstrated is the matching of
drug-likemoleculesagainsta largedatabaseofmoleculesthat havepotential
anti-cancer properties. In order to improve the speed of search, the author
also describes innovative methods for implementing the graph matcher in
computer hardware
Randall, Hendtlass and Lewis, in “Extremal Optimisation of Assignment
Type Problems”, present a theoretical and practical exposition of the ca-
pabilities of the nature-inspired Extremal Optimisation. They extend the
extremal optimisation metaphor so that it is able to handle constraints and
reduce solutioninfeasibility in a standardway.In addition, a partially adap-
tive population model is also presented. Results of empirical investigations
reveal that this simple meta-heuristic is very competitve with more estab-
lished optimisation techniques, for a range of assignment type problems.
Preface VII
Enhancing another meta-heuristic, ant colony optimisation, is the subject
of Angus’ “Niching for Ant Colony Optimisation”. Niching is a technique
derived from the biologicalnotion that different species will specialise in the
exploitation of different parts of the environment. In the computational and
optimisationsense,itreferstodifferentindividualsorpopulationsofsolutions
exploring different parts of the search space, thus ensuring sufficient overall
diversity. Two alternative forms of niching, based on crowding and fitness
sharing concepts, are shown to be particularly effective for multi-modal and
multi-objective problems.
Theremainderofthebookisconcernedwiththeapplicationofbiologically
inspiredoptimisationmethods to problemsthatcommonlyoccurinindustry
and the sciences. These particularly demonstrate that improved and novel
solutions are capable of being generated to problems that have been tradi-
tionally exclusively the domain of human experts.
Computational optimisation found early adoption in the field of engineer-
ing design and manufacture. The design of radio antennas is an area that
has historically been dominated by the considerable use of domain expertise
and analytic solution methods. In recent years there has been an explosion
of interest in automated design by the use of optimisation meta-heuristics,
extending the ability of engineers to consider previously intractable prob-
lems. An example is the use of ant colony optimisation for the construc-
tion of compact meander line antennas for Radio Frequency IDentification
(RFID) devices. Lewis, Randall, Galehdar, Thiel and Weis, in “Using Ant
Colony Optimisation to ConstructMeander-line RFID Antennas”, presenta
developmental history of how the authors have solved this real-world prob-
lem - from initial application, the use of a novel local refinement technique
and finally a multi-objective version that is able to optimise both antenna
efficiency and resonant frequency. Results from computational experiments
demonstrate the significant improvements that can be achieved.
A very practical problem in the area of establishing communication in-
frastructures is the radio network design problem. Mendes, Go´mez-Pulido,
Vega-Rodr´ıguez,S´anchez-P´erez,S´aezandIsasi,intheirchapterentitled“The
Radio Network Design Optimization Problem”, examine how a number of
different biologically inspired meta-heuristics, including GRASP, genetic al-
gorithms and memetic algorithms, perform on a large and difficult network
design problem. Radio network design, in general, is an NP-complete prob-
lem and while a number of different approaches have been used to address
it all lack a comparable measure of efficiency. Mendes et al. offer a reliable
benchmark reference, and use it to investigate different algorithms and the
reproducibility of their results.
A different form of network problem is the distribution of electricity
through power grids. In particular, the issue of the imbalance between pre-
dicted electricity use and actual consumption is of great importance when
reducing greenhouse gas emissions. Kamper and Eßer’s chapter “Strategies
for Decentralised Balancing Power” shows how a self-organising approach,
VIII Preface
basedon evolutionaryalgorithms,canreduce this imbalance by dynamically
poolingsmallelectricaldevices(suchaswashingmachinesandcombinedheat
and power plants) together.
Conformational sampling, the prediction of the three-dimensional shapes
ofmoleculesbasedontheircompositionandconnectivity,isacentralproblem
instructuralbiologyanddrugdesign.Thereisacontinuingsearchforgeneral
approaches to finding the most stable molecular geometries. In “An Analy-
sis of Dynamic Mutation Operators for Conformational Sampling” Tantar,
Melaband Talbiuse this problemas a case study to examine the use ofana
priori mutation operator selection and parameter tuning phase prior to ex-
ecution of an evolutionary algorithm. They conclude, in part, that dynamic
approaches,possibly including self-adaptive schemes, hold the most promise
for tackling these extremely difficult problems.
In contrast to the preceding chapters on application of optimisation al-
gorithms to problems in the physical sciences the book closes with a study
fromthefieldofartificialintelligence(AI).Whilemuchattentionhasbeenfo-
cussedontheuseofAIforplayingchess,thechapterbyQuek,Chan,Tanand
Tay, “Evolving Computer Chinese Chess using Guided Learning” examines
evolutionary algorithms applied to playing Chinese chess. They explore how
different heuristics, and indeed the knowledge of grandmasters of the game,
can be used and integrated with genetic algorithms in order to produce an
artificial player that can realistically challenge human opponents.
The underlying problems that the methods and techniques discussed in
this book address are typically complex and demanding. In particular, the
computationalrequirementscanoftenbe considerableandsoeffortsmustbe
madetoprovidesufficientcomputingcapacitytomeettheseneeds.Currently,
this generally makes parallel computing a necessity, and this is a consistent
theme of the approaches considered by the contributing authors, whether
explicitly, as in the overview of Jaimes and Coello Coello, or implicit in the
natureofthemethodsadoptedbyseveralothers:thepopulation-basedmeth-
ods of, for example, particle swarmand antcolony optimisationalgorithms,
and such techniques as neuralnetworks.Indeed, the drive for computational
performance can be seen in the implementation of algorithms in hardware
described by Austin.
The forminwhichparallelcomputingresourcesareprovidedcanbringits
own set of challenges. The search for cost-effective means of accessing large
computational capacity has given rise to a trend towardgrid computing and
distributed, peer-to-peer computing environments. Highly dynamic, hetero-
geneous and prone to failure, these resources demand regard for the fault
toleranceofoptimisationalgorithms,andeffortstoaddressthisissuesuchas
those of Lewis, Mostaghim and Scriven will become increasingly important
as the approaches become more widely employed.
The editors wish to acknowledgea number of groups and individuals that
helpedtomakethisprojectrealisable.Firstofall,wewishtothankProfessor
JanuszKacprzyk,EditorinChiefoftheStudiesinComputationalIntelligence
Preface IX
series,for initially proposing the projectand his continuing support. The se-
riesteamatSpringer-Verlag,inparticularDrThomasDitzingerandHeather
King, are to be thanked for all their helpful advice and support. Along with
them, we pay tribute to the work of the authors. Their outstanding ideas
will resonate with the optimisation community in years to come. Finally, we
thank the external reviewers for their astute comments and suggestions on
each of the chapters.
February 2009
Brisbane, Australia, Andrew Lewis
Karlsruhe, Germany, and Sanaz Mostaghim
Gold Coast, Australia Marcus Randall
Contents
Evolution’s Niche in Multi-Criterion Problem Solving ....... 1
Kalyanmoy Deb
Applications of Parallel Platforms and Models in
Evolutionary Multi-Objective Optimization.................. 23
Antonio L´opez Jaimes, Carlos A. Coello Coello
Asynchronous Multi-Objective Optimisation in Unreliable
Distributed Environments ................................... 51
Andrew Lewis, Sanaz Mostaghim, Ian Scriven
Dynamic Problems and Nature Inspired Meta-heuristics..... 79
Tim Hendtlass, Irene Moser, Marcus Randall
Relaxation Labelling Using Distributed Neural Networks .... 111
Jim Austin
Extremal Optimisation for Assignment Type Problems ...... 139
Marcus Randall, Tim Hendtlass, Andrew Lewis
Niching for Ant Colony Optimisation ........................ 165
Daniel Angus
Using Ant Colony Optimisationto Construct Meander-Line
RFID Antennas ............................................. 189
Andrew Lewis, Marcus Randall, Amir Galehdar, David Thiel,
Gerhard Weis
The Radio Network Design Optimization Problem:
Benchmarking and State-of-the-Art Solvers.................. 219
S´ılvioP.Mendes,JuanA.Go´mez-Pulido,MiguelA.Vega-Rodr´ıguez,
Juan M. Sa´nchez-P´erez, Yago S´aez, Pedro Isasi