Evolutionary Algorithms, Swarm Dynamics and Complex Networks: Methodology, Perspectives and Implementation

322 Pages·2018·35.66 MB·English

by  Ivan Zelinka

Emergence, Complexity and Computation ECC
Ivan Zelinka
Guanrong Chen Editors
Evolutionary
Algorithms, Swarm
Dynamics and
Complex Networks
Methodology, Perspectives and
Implementation
Emergence, Complexity and Computation
Volume 26
Series editors
Ivan Zelinka, Technical University of Ostrava, Ostrava, Czech Republic
e-mail: [email protected]
Andrew Adamatzky, University of the West of England, Bristol, UK
e-mail: [email protected]
Guanrong Chen, City University of Hong Kong, Hong Kong, China
e-mail: [email protected]
Editorial Board
Ajith Abraham, MirLabs, USA
Ana Lucia C. Bazzan, Universidade Federal do Rio Grande do Sul, Porto Alegre,
RS, Brazil
Juan C. Burguillo, University of Vigo, Spain
Sergej Čelikovský, Academy of Sciences of the Czech Republic, Czech Republic
Mohammed Chadli, University of Jules Verne, France
Emilio Corchado, University of Salamanca, Spain
Donald Davendra, Technical University of Ostrava, Czech Republic
Andrew Ilachinski, Center for Naval Analyses, USA
Jouni Lampinen, University of Vaasa, Finland
Martin Middendorf, University of Leipzig, Germany
Edward Ott, University of Maryland, USA
Linqiang Pan, Huazhong University of Science and Technology, Wuhan, China
Gheorghe Păun, Romanian Academy, Bucharest, Romania
Hendrik Richter, HTWK Leipzig University of Applied Sciences, Germany
Juan A. Rodriguez-Aguilar, IIIA-CSIC, Spain
Otto Rössler, Institute of Physical and Theoretical Chemistry, Tübingen, Germany
Vaclav Snasel, Technical University of Ostrava, Czech Republic
Ivo Vondrák, Technical University of Ostrava, Czech Republic
Hector Zenil, Karolinska Institute, Sweden
About this Series
The Emergence, Complexity and Computation (ECC) series publishes new
developments, advancements and selected topics in the fields of complexity,
computation and emergence. The series focuses on all aspects of reality-based
computation approaches from an interdisciplinary point of view, especially from
applied sciences, biology, physics, or chemistry. It presents new ideas and
interdisciplinary insight on the mutual intersection of subareas of computation,
complexity and emergence and its impact and limits to any computing based on
physical limits (thermodynamic and quantum limits, Bremermann’s limit, Seth
Lloyd limits…) as well as algorithmic limits (Gödel’s proof and its impact on
calculation, algorithmic complexity, Chaitin’s Omega number and Kolmogorov
complexity, non-traditional calculations like Turing machine process and its
consequences,…) and limitations arising in the artificial intelligence field. The
topics are (but not limited to) membrane computing, DNA computing, immune
computing, quantum computing, swarm computing, analogic computing, chaos
computingandcomputingontheedgeofchaos,computationalaspectsofdynamics
of complex systems (systems with self-organization, multiagent systems, cellular
automata, artificial life,…), emergence of complex systems and its computational
aspects,aswellasagentbasedcomputation.Theaimofthisseriesistodiscussthe
above-mentioned topics from an interdisciplinary point of view and present new
ideas coming from the mutual intersection of classical as well as modern methods
of computation. Within the scope of the series are monographs, lecture notes,
selected contributions from specialized conferences and workshops, special
contribution from international experts.
More information about this series at http://www.springer.com/series/10624
Ivan Zelinka Guanrong Chen
(cid:129)
Editors
Evolutionary Algorithms,
Swarm Dynamics
and Complex Networks
Methodology, Perspectives
and Implementation
123
Editors
IvanZelinka Guanrong Chen
Department ofComputer Science Department ofElectronic Engineering
Faculty of Electrical Engineering City University of HongKong
andComputer ScienceVŠB-TUO Kowloon, HongKong
Ostrava, Poruba China
Czech Republic
ISSN 2194-7287 ISSN 2194-7295 (electronic)
Emergence, Complexity andComputation
ISBN978-3-662-55661-0 ISBN978-3-662-55663-4 (eBook)
https://doi.org/10.1007/978-3-662-55663-4
LibraryofCongressControlNumber:2017948219
©Springer-VerlagGmbHGermany2018
Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpart
of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations,
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The use of general descriptive names, registered names, trademarks, service marks, etc. in this
publicationdoesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfrom
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The publisher, the authors and the editors are safe to assume that the advice and information in this
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for any errors or omissions that may have been made. The publisher remains neutral with regard to
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Printedonacid-freepaper
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TheregisteredcompanyisSpringer-VerlagGmbHGermany
Theregisteredcompanyaddressis:HeidelbergerPlatz3,14197Berlin,Germany
Ivan Zelinka dedicates this book to his family
and parents.
Guanrong Chen dedicates this book to the
memory of his mentor Professor Mingjun
Chen (1934–2008).
Foreword
SeveralnaturalprocessesincludingDarwinianevolution,thecollectivebehaviorof
socialcreaturesandtheirforagingstrategiesarecenteredaroundtheclassicaltaskof
optimization. For more than half a century now, researchers have been drawing
inspirations from the life-supporting activities and adaptation mechanisms of nat-
ural creatures to design algorithms that can solve complex and mathematically
intractablesearchandoptimizationproblemswhichareubiquitousindisciplinesof
science and technology. Currently, the field of such nature-inspired algorithms is
growing at a spectacular rate, and new algorithmic variants are continually
emerging to meet the fast-growing challenges of the real-world optimization
problems,forwhichnomathematicallyguaranteedmethodsareavailable.Thetwo
main families of algorithms that primarily constitute this field today are the evo-
lutionary computing methods and the swarm intelligence algorithms.
The book edited by Profs. Ivan Zelinka and Guanrong Chen takes a very dif-
ferent and elegant view of the fundamental algorithms belonging to evolutionary
computing and swarm intelligence: how to obtain an insight into the dynamics of
such algorithms by modeling them through the dynamics of an equivalent social
network? This view enables researchers to gain valuable information about the
search dynamics of these algorithms, thereby predicting the useful ranges of the
associated control parameters and applicability to various real-life problems, by
analyzing the equivalent social network. The book presents a well-organized col-
lection of 14 comprehensive chapters divided into three parts. The reader is care-
fullynavigatedthroughtheefficaciesofcomplexnetworks,swarmandevolutionary
dynamics, and their randomization aspects. The exposure of the material is lucid.
Quite complicated concepts are presented in a clear and convincing way which
attributedtotheexpertiseofthechapterauthorsandtheEditors.Thefinalchapters
ofthebook(likeChaps.11and12)provideveryinterestingextensionsoftheideas
presentedpreviouslytowardsmorepracticalscenarios,forexample,Chap.11deals
with the dynamics and communications of swarm virus seen through the lens of
complex networks. In the exposure of the material, the authors have achieved a
sound balance between the theory and practice.
vii
viii Foreword
This book is the first of its kind, presenting a very interesting intersection of
three fast-growing research fields of the swarm and evolutionary computing,
complex networks, and CML systems. The idea of their mutual intersection is not
very typical in the existing literature, and this is probably one of the main reasons
why this edition should be especially valuable for the scientific and engineering
research community.
Finally,Imustconcludethatthisisnotonlyanurgentlyneededandverytimely
volume, but also an authoritative and exceptionally well-compiled treatise of the
fascinating topic of unification of the meta-heuristic dynamics and complex
networks.
Swagatam Das
Indian Statistical Institute, Kolkata, India
Preface
Evolutionary algorithms constitute a class of well-known numerical methods,
which are based on the Darwinian theory of evolution and Mendelian theory of
heritage. They are partly based on random and partly based on deterministic
principles.Duetothisnature,itischallengingtopredictitsperformanceinsolving
complex nonlinear problems. Many techniques and hybridization methods have
been developed to improve the algorithmic performances. These methods are
typicallybasedonstatisticalapproachesandusuallyleadtoarecommendedsetting
foragivenalgorithmoraclassofalgorithms.Also,verydiversehybridizationsare
suggested by utilizing deterministic chaos instead of using other pseudorandom
number generators, showing promising features and unique advantages. Recently,
the study of evolutionary dynamics is focused not only on the traditional investi-
gations, but also on the understanding and analyzing new principles, with the
intentionofcontrollingandutilizingtheirpropertiesandperformancestowardmore
effective real-world applications.
This book, based on many years of intensive research of the authors, is
proposing novel ideas about advancing evolutionary dynamics toward new phe-
nomena including many new topics, even the dynamics of equivalent social net-
works.Infact,itincludesmoreadvancedcomplexnetworksandincorporatesthem
with the CMLs (coupled map lattices), which are usually used for spatiotemporal
complex systems simulation and analysis, based on the observation that chaos in
CML can be controlled, so does evolution dynamics. It will be shown that evo-
lutionary algorithmscanbeunderstood justlikedynamicalsystemswith feedback.
Thus, at least in theory, all engineering control methods can be applied. All such
ideas will be illustrated and discussed in the following chapters. All the chapter
authorsare,tothebestofourknowledge,originatorsoftheideasmentionedabove
and researchers on evolutionary algorithms and chaotic dynamics as well as
complex networks, who will provide benefits to the readers regarding modern
scientific research on related subjects.
ix
x Preface
Theorganizationofthechaptersinthebookisasfollows.Thebookconsistsof
three parts. The first part (Theory) discusses and explains basic ideas about swarm
dynamics and evolutionary algorithms related to complex networks and CML
systems.Chapter1presentsmostimportantnotionswithcomprehensivereferences.
Chapter2discusseshowtocreatenetworksfromevolutionarydynamics,basedon
a few selected evolutionary algorithms, like ant colony optimization, with original
experimentsandvisualizations.Thesecondpart(Applications)showshowtheidea
abovecanbeappliedtodevelopingvariouseffectivealgorithmsandwhatlevelsof
success it can reach to. Chapter 3 reports the use of the differential evolution
algorithms and its conversion into networks with performance improvements.
Chapters 4–6 explain, in more details, the conversion, analysis, and improvement
oftheSOMAalgorithmusingthecomplexnetworkframework.InChap.7,theuse
of complex networks in particle swarm algorithms is discussed, followed by an
investigationofartificialbeecolonyalgorithmsinChap.8.Chapter9thenpresents
differentviewsonhowrandomizationandcomplexnetworkscanbeconstructedfor
meta-heuristic algorithms. The last part (Miscellanies) contains a few interesting
chapters as possible extensions of the above-discussed ideas to other directions.
Chapter 11 discusses possibilities for dynamics and communications of swarm
computer viruses to be visualized as a network. This can be necessary for its
analysis and prevention in the future. Today, the most advanced virus-attacking
technology is perhaps Botnet or viruses developed based on the CnC (command
andcontrol)technology,e.g.,StuxnetorGauss.Suchnewviraltechnologiescanbe
usednotonlyforswarmintelligence,butalsofortheevolutionofviruscodes.This
chapter predicts the future merging of technologies such as swarm intelligence,
evolution dynamics, and complex networks. Chapter 12 further explains how
networks are related totheway they areextended tocellular automata. Chapter 13
studies the topic of this book but from an opposite point of view as for how
evolutionary dynamics can be used to design power grid networks. Chapter 14
discusses the dynamic analysis of genetic regulatory networks which can be an
inspiration to be applied to topics mentioned above.
Regarding the readership of the book, it presents instructional materials for
senior undergraduate and graduate students in computer science, physics, applied
mathematics and engineering, among others, who are working in the fields of
complex networks and evolutionary algorithms, and even chaotic dynamics.
Researchers who want to learn more on how evolutionary algorithms can be con-
structed, analyzed, or controlled, as well as the relationships among swarm
dynamics, complex networks, and CML systems, will find this book very useful.
Thebookwillbearesourcehandbookandmaterialcollectionforpractitionerswho
wanttoapplythesemethodstosolvereal-lifeproblemsinchallengingapplications.
Thisbookisbynomeanscomprehensiveonthethreefieldsofresearchduetoits
pagelimitation.Onlyselectedbasicideasandmainresultsarereported.Forfurther