Table Of ContentIvan 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
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.
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.
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
info, it is recommended to read referenced literature, which contains all relevant
researchresultsandthelatestresearchprogress.Theeditorsandthechapterauthors
hope that the readers will find the book informative and valuable for their studies,
experiments, and simulations.
Ostrava, Czech Republic Ivan Zelinka
Kowloon, Hong Kong Guanrong Chen
June 2017
Contents
Part I Theory
1 Swarm and Evolutionary Dynamics as a Network. .... ..... .... 3
Ivan Zelinka
2 Evolutionary Dynamics and Its Network Visualization - Selected
Examples . .... .... .... ..... .... .... .... .... .... ..... .... 31
Orkhan Yarakhmedov, Victor Polyakh, Ivan Chernogorov
and Ivan Zelinka
Part II Applications
3 Differential Evolution Dynamics Modeled by Social Networks ........ 67
Lenka Skanderová and Ivan Zelinka
4 Conversion of SOMA Algorithm into Complex Networks .... .... 101
Lukáš Tomaszek and Ivan Zelinka
5 Analysis of SOMA Algorithm Using Complex Network. ..... .... 115
Lukáš Tomaszek and Ivan Zelinka
6 Improvement of SOMA Algorithm Using Complex Networks. .... 131
Lukáš Tomaszek and Ivan Zelinka
7 Complex Networks in Particle Swarm... .... .... .... ..... .... 145
Michal Pluhacek, Roman Šenkeřík, Adam Viktorin
and Tomas Kadavy
8 Comparison of Vertex Centrality Measures inComplexNetwork
Analysis Based on Adaptive Artificial Bee Colony Algorithm . .... 161
Magdalena Metlicka and Donald Davendra
9 Randomization and Complex Networks for Meta-Heuristic
Algorithms.... .... .... ..... .... .... .... .... .... ..... .... 177
Roman Šenkeřík, Ivan Zelinka, Michal Pluhacek, Adam Viktorin,
Jakub Janostik and Zuzana Kominkova Oplatkova
10 Gallery of Evolutionary Networks.. .... .... .... .... ..... .... 195
Ivan Zelinka, Roman Šenkeřík and Michal Pluháček
Part III Miscellanies
11 Swarm Virus, Evolution, Behavior and Networking.... ..... .... 213
Lubomir Sikora and Ivan Zelinka
12 Simple Networks on Complex Cellular Automata: From de
Bruijn Diagrams to Jump-Graphs.. .... .... .... .... ..... .... 241
Genaro J. Martínez, Andrew Adamatzky, Bo Chen, Fangyue Chen
and Juan C. Seck-Tuoh-Mora
13 AHybridMulti-objectiveEvolutionaryApproachforPowerGrid
Topology Design ... .... ..... .... .... .... .... .... ..... .... 265
Xiaowen Bi and Wallace K.S. Tang
14 Dynamic Analysis of Genetic Regulatory Networks with Delays..... . 285
Zhi-Hong Guan and Guang Ling
15 Frontiers . .... .... .... ..... .... .... .... .... .... ..... .... 311
Ivan Zelinka
Acronyms
ABC Artificial bee colony, heuristic algorithm
ACO Ant colony optimization, heuristic algorithm based on
biological ant behavior
Best The best individual, DE
Betweenness centrality Graph attributes
CA Cellular automata
CF Cost function, used to evaluate individual quality
Chaotic map Simple iterative description of chaotic systems
Closeness centrality Graph attributes
CML Coupled map lattices, simple model used to simulate
spatiotemporal deterministic chaos
CnC Command and control, technology used to control
computer viruses in a centralized manner
CNCML CMLsystemthatiscreatedonschemeEA!CN!CML
CN Complex network, graph (network) with nontrivial
topological features—features that do not occur in
simple networks such as lattices or random graphs,
usually occur in graphs modeling of real systems.
CNS Complex network structure
CPRNG Chaotic pseudorandom number generator, deterministic
chaos is used here instead of classical formulas for
PRNG
CR Crossover threshold, DE
DE Differential evolution
Degree centrality Graph attributes
EA Evolutionary algorithms
ECT Evolutionary computational techniques
EP Evolutionary programming
ES Evolutionary strategies
