Handbook of Swarm Intelligence: Concepts, Principles and Applications

537 Pages·2010·17.36 MB·English

by  Maurice Clerc (auth.)

BijayaKetanPanigrahi,YuhuiShi,andMeng-HiotLim(Eds.)
HandbookofSwarmIntelligence
Adaptation,Learning,andOptimization,Volume8
SeriesEditor-in-Chief
Meng-HiotLim
NanyangTechnologicalUniversity,Singapore
E-mail:[e-mail]
Yew-SoonOng
NanyangTechnologicalUniversity,Singapore
E-mail:[e-mail]
Furthervolumesofthisseriescanbefoundonourhomepage:springer.com
Vol.1.JingqiaoZhangandArthurC.Sanderson
AdaptiveDifferentialEvolution,2009
ISBN978-3-642-01526-7
Vol.2.YoelTenneandChi-KeongGoh(Eds.)
ComputationalIntelligencein
ExpensiveOptimizationProblems,2010
ISBN978-3-642-10700-9
Vol.3.Ying-pingChen(Ed.)
ExploitationofLinkageLearninginEvolutionaryAlgorithms,2010
ISBN978-3-642-12833-2
Vol.4.AnyongQingandChingKwangLee
DifferentialEvolutioninElectromagnetics,2010
ISBN978-3-642-12868-4
Vol.5.RuhulA.SarkerandTapabrataRay(Eds.)
Agent-BasedEvolutionarySearch,2010
ISBN978-3-642-13424-1
Vol.6.JohnSeifferttandDonaldC.Wunsch
UnifiedComputationalIntelligenceforComplexSystems,2010
ISBN978-3-642-03179-3
Vol.7.YoelTenneandChi-KeongGoh(Eds.)
ComputationalIntelligenceinOptimization,2010
ISBN978-3-642-12774-8
Vol.8.BijayaKetanPanigrahi,YuhuiShi,andMeng-HiotLim(Eds.)
HandbookofSwarmIntelligence,2011
ISBN978-3-642-17389-9
Bijaya Ketan Panigrahi,Yuhui Shi,
and Meng-Hiot Lim (Eds.)
Handbook of Swarm Intelligence
Concepts,Principles and Applications
123
Dr.BijayaKetanPanigrahi Dr.Meng-HiotLim
ElectricalEngineeringDepartment SchoolofElectrical&ElectronicEngineering
IndianInstituteofTechnology,Delhi NanyangTechnologicalUniversity
NewDelhi,India–110016 Singapore639798
E-mail:[e-mail] E-mail:[e-mail]
Prof.YuhuiShi
DirectorofResearchandPostgraduateOffice
Xi’anJiaotong-LiverpoolUniversity,
Suzhou,China215123
E-mail:[e-mail]
ISBN 978-3-642-17389-9 e-ISBN 978-3-642-17390-5
DOI 10.1007/978-3-642-17390-5
Adaptation, Learning, and Optimization ISSN1867-4534
(cid:2)c 2011 Springer-Verlag Berlin Heidelberg
This work is subject to copyright. All rights are reserved, whether the whole or part
of the material is concerned, specifically the rights of translation, reprinting, reuse of
illustrations, recitation, broadcasting, reproduction on microfilm or in any other way,
andstorageindatabanks.Duplicationofthispublicationorpartsthereofispermitted
only under the provisions of the German Copyright Law of September 9, 1965, in
its current version, and permission for use must always be obtained from Springer.
Violations are liable to prosecution undertheGerman Copyright Law.
Theuseofgeneral descriptivenames,registered names,trademarks,etc.inthispubli-
cationdoesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesare
exemptfromtherelevantprotectivelawsandregulationsandthereforefreeforgeneral
use.
Typeset&CoverDesign:ScientificPublishing Services Pvt. Ltd., Chennai, India.
Printed on acid-free paper
9 8 7 6 5 4 3 2 1
springer.com
Preface

Swarm Intelligence is a collection of nature-inspired algorithms under the big um-
brella of evolutionary computation. They are population based algorithms. A popula-
tion of individuals (potential candidate solutions) cooperating among themselves and
statistically becoming better and better over generations and eventually finding (a)
good enough solution(s).
Researches on swarm intelligence generally fall into three big categories. The first
category is on algorithm. Each swarm intelligence algorithm has been studied and
modified to improve its performance from different perspectives such as the conver-
gence, solution accuracy, and algorithm efficiency, etc. For example, in particle
swarm optimization (PSO) algorithm, the impacts of inertia weight, acceleration coef-
ficients, and neighborhood on the performance of the algorithm have been studied
extensively. Consequently, all kind of PSO variants have been proposed. The second
category is on the types of problems that the algorithms are designed and/or modified
to solve. Generally speaking, most of the swarm intelligence algorithms are originally
designed to solve unconstraint single-objective optimization problems. The algo-
rithms are then studied and modified to suit for solving other types of problems such
as constraint single-objective optimization problems, multi-objective optimization
problems, constraint multi-objective optimization problems, and combinatorial opti-
mization problems, etc.. The third category is on algorithms’ applications. Swarm
intelligence algorithms have been successfully applied to solve all kinds of problems
covering a wide range of real-world applications. Due to algorithms’ characteristics
such as that they usually do not require continuity and differentiability which are
critical for traditional optimization algorithms to have, swarm intelligence algorithms
have been able to solve a lot of real-world application problems which are very diffi-
cult, if not impossible, for traditional algorithms to solve. Therefore, swarm intelli-
gence algorithms have been attracting more and more attentions from engineers in all
industrial sectors. It is the successful real-world applications that are the impetus and
vitality that drives the research on swarm intelligence forward.
A research trend of swarm intelligence is on hybrid algorithms. A swarm intelli-
gence algorithm is combined with other swarm intelligence algorithms, evolutionary
algorithms, or even other traditional algorithms to take advantage of strengths from
each algorithm to improve algorithms’ performance for more effective and efficient
problem-solving. Initially and intuitively, a swarm intelligence algorithm such as
VI Preface
particle swarm optimization algorithm is used as a global search method to find good
initial starting point(s), and then a local method such as greedy algorithm, hill-
climbing algorithms, etc. is employed to search for the better solution from the initial
starting point(s). Currently a new concept of memetic computation is becoming popu-
lar. Within the memetic computation framework, memetic algorithms is arguably the
most popular. In memetic algorithms, two levels of learning processes are involved:
one on learning capability, another on knowledge (individual) learning. Over genera-
tions, each individual evolves to have better learning capability. Within each genera-
tion, each individual learns knowledge in its neighborhood. Currently, the individuals
at two different learning levels generally use the same representation. But it is reason-
able to expect different representation for individuals at different level of learning.
Another research trend is on solving large-scale problems. Similar to the traditional
algorithms for solving large scale problems, swarm intelligence algorithms also suffer
the “dimensionality curse”. Researchers are actively engaging in applying swarm in-
telligence algorithms to solve large-scale optimization problems.
In recent years, swarm intelligence algorithms have become a very active and fruit-
ful research area. There are journals dedicated to swarm intelligence. There are also
international conferences held annually that are dedicated to swarm intelligence. The
number of papers published in both journals and conferences are increasing exponen-
tially. It is therefore the right time to commit a book volume dedicated to swarm intel-
ligence. Due to the limited space, this book does not intend to cover all aspects of
research work on swam intelligence. More appropriately, it is a snapshot of current
research on swarm intelligence. It covers particle swarm optimization algorithm, ant
colony optimization algorithm, bee colony optimization algorithm, bacterial foraging
optimization algorithm, cat swarm optimization algorithm, harmony search algorithm,
and their applications.
This book volume is primarily intended for researchers, engineers, and graduate
students with interests in swarm intelligence algorithms and their applications. The
chapters cover different aspects of swarm intelligence, and as a whole should provide
broad perceptive insights on what swarm intelligence has to offer. It is very suitable
as a graduate level text whereby students may be tasked with the study of the rich
variants of swarm intelligence techniques coupled with a project type assignment that
involves a hands-on implementation to demonstrate the utility and applicability of
swarm intelligence techniques in problem-solving.
The various chapters are organized into 4 parts for easy reference. Part A grouped
together 12 chapters, forming the most substantial part of this volume. The focus is
primarily on particle swarm optimization. Besides the first two chapters that focus on
general issues pertaining to particle swarm optimization, the remaining of the chapters
in Part A offer a good mix of work, with some focusing on analytical issues and the
remaining on applications. Part B has 3 chapters on honey bee algorithms, one of
which describes the application of honey bee algorithm in vehicle routing problem.
Part C covers ant colony optimization, a long established swarm technique. The two
chapters in this part should serve as a gentle reminder to the fact that established or
matured swarm techniques such as ant colony optimization can be combined innova-
tively with other swarm or conventional techniques to further enhance its problem-
solving capability. In Part D, we grouped together the various swarm techniques,
including work that combines two swarm techniques, otherwise referred to as hybrid
Preface VII
swarm intelligence. This serves as a glimpse of the possibilities of swarm techniques
inspired by the metaphors of swarming behavior in nature. Glowworm optimization,
bacterial foraging optimization and cat swarm optimization are examples to this
effect.
We are grateful to the authors who have put in so much effort to prepare their
manuscripts, sharing their research findings as a chapter in this volume. Based on
compilation of chapters in this book, it is clear that the authors have done a stupen-
dous job, each chapter directly or indirectly hitting on the spot certain pertinent aspect
of swarm intelligence. As editors of this volume, we have been very fortunate to have
a team of Editorial Board members who besides offering useful suggestions and
comments, helped to review manuscripts submitted for consideration. Out of the
chapters contributed and finally selected for the volume, 4 were honored with the
Outstanding Chapter Award. The chapters selected for the awards were decided with
the help of the Editorial Board members. The original intention was to choose only 3.
However, due to a tie in ranking scores submitted by the Editorial Board members, it
was deemed as more appropriate to grant 4 awards instead. We must emphasize that
this had not been easy as many of the chapters contributed are just as deserving.
Finally, we would like to put in no definitive order our acknowledgements of the vari-
ous parties who played a part in the successful production of this volume:
- Authors who contributed insightful and technically engaging chapters in this
volume;
- To the panel of experts in swarm intelligence that form Editorial Board members,
a big thank you;
- Our heartiest Congratulations to the authors of the 4 chapters honored with the
Outstanding Chapter Award;
- The publisher, in particular Tom Ditzinger and his team have been very helpful
and accommodating, making our tasks a little easier.

Editors

Yuhui Shi
Meng-Hiot Lim
Bijaya Ketan Panigrahi
Contents
Part A: Particle Swarm Optimization
From Theory to Practice in Particle Swarm Optimization ...... 3
Maurice Clerc
What Makes Particle Swarm Optimization a Very Interesting
and Powerful Algorithm? ....................................... 37
J.L. Ferna´ndez-Mart´ınez, E. Garc´ıa-Gonzalo
Developing Niching Algorithms in Particle Swarm
Optimization ................................................... 67
Xiaodong Li
Test Function Generators for Assessing the Performance of
PSO Algorithms in Multimodal Optimization................... 89
Julio Barrera, Carlos A. Coello Coello
Linkage Sensitive Particle Swarm Optimization ................. 119
Deepak Devicharan, Chilukuri K. Mohan
Parallel Particle Swarm Optimization Algorithm Based on
Graphic Processing Units....................................... 133
Ying Tan, You Zhou
Velocity Adaptation in Particle Swarm Optimization ........... 155
Sabine Helwig, Frank Neumann, Rolf Wanka
Integral-Controlled Particle Swarm Optimization ............... 175
Zhihua Cui, Xingjuan Cai, Ying Tan, Jianchao Zeng
Particle Swarm Optimization for Markerless Full Body
Motion Capture ................................................ 201
Zheng Zhang, Hock Soon Seah, Chee Kwang Quah
X Contents
An Adaptive Multi-Objective Particle Swarm Optimization
Algorithm with Constraint Handling ........................... 221
Praveen Kumar Tripathi, Sanghamitra Bandyopadhyay,
Sankar Kumar Pal
Multiobjective Particle Swarm Optimization for Optimal
Power Flow Problem ........................................... 241
M.A. Abido
A Multi-objective Resource Assignment Problem in Product
Driven Supply Chain Using Quantum Inspired Particle Swarm
Algorithm ...................................................... 269
Sri Krishna Kumar, S.G. Ponnambalam, M.K. Tiwari
Part B: Bee Colony Optimization
Honeybee Optimisation – An Overview and a New Bee
Inspired Optimisation Scheme .................................. 295
Konrad Diwold, Madeleine Beekman, Martin Middendorf
Parallel Approaches for the Artificial Bee Colony
Algorithm ...................................................... 329
Rafael Stubs Parpinelli, C´esar Manuel Vargas Benitez,
Heitor Silv´erio Lopes
Bumble Bees Mating Optimization Algorithm for the Vehicle
Routing Problem ............................................... 347
Yannis Marinakis, Magdalene Marinaki
Part C: Ant Colony Optimization
Ant Colony Optimization: Principle, Convergence and
Application..................................................... 373
Haibin Duan
Optimization of Fuzzy Logic Controllers for Robotic
Autonomous Systems with PSO and ACO ...................... 389
Oscar Castillo, Patricia Melin, Fevrier Valdez,
Ricardo Mart´ınez-Marroqu´ın
Part D: Other Swarm Techniques
A New Framework for Optimization Based-On Hybrid Swarm
Intelligence ..................................................... 421
Pei-Wei Tsai, Jeng-Shyang Pan, Peng Shi, Bin-Yih Liao
Contents XI
Glowworm Swarm Optimization for Multimodal Search
Spaces.......................................................... 451
K.N. Krishnanand, D. Ghose
Direct and Inverse Modeling of Plants Using Cat Swarm
Optimization ................................................... 469
Ganapati Panda, Pyari Mohan Pradhan, Babita Majhi
Parallel Bacterial Foraging Optimization........................ 487
S.S. Pattnaik, K.M. Bakwad, S. Devi, B.K. Panigrahi, Sanjoy Das
Reliability-Redundancy Optimization Using a Chaotic
Differential Harmony Search Algorithm......................... 503
Leandro dos Santos Coelho, Diego L. de A. Bernert,
Viviana Cocco Mariani
Gene Regulatory Network Identification from Gene
Expression Time Series Data Using Swarm Intelligence ......... 517
Debasish Datta, Amit Konar, Swagatam Das, B.K. Panigrahi
Author Index................................................... 543