Recent Advances in Swarm Intelligence and Evolutionary Computation

303 Pages·2015·11.35 MB·English

by  Xin-She Yang (eds.)

Studies in Computational Intelligence 585
Xin-She Yang E ditor
Recent Advances
in Swarm
Intelligence and
Evolutionary
Computation
Studies in Computational Intelligence
Volume 585
Series editor
Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
e-mail: [email protected]
About this Series
The series “Studies in Computational Intelligence” (SCI) publishes new develop-
mentsandadvancesinthevariousareasofcomputationalintelligence—quicklyand
with a high quality. The intent is to cover the theory, applications, and design
methods of computational intelligence, as embedded in the fields of engineering,
computer science, physics and life sciences, as well as the methodologies behind
them. The series contains monographs, lecture notes and edited volumes in com-
putational intelligence spanning the areas of neural networks, connectionist
systems, genetic algorithms, evolutionary computation, artificial intelligence, cel-
lularautomata,self-organizingsystems,softcomputing,fuzzysystems,andhybrid
intelligent systems. Of particular value to both the contributors and the readership
are the short publication timeframe and the world-wide distribution, which enable
both wide and rapid dissemination of research output.
More information about this series at http://www.springer.com/series/7092
Xin-She Yang
Editor
Recent Advances in Swarm
Intelligence and
Evolutionary Computation
123
Editor
Xin-She Yang
School ofScience and Technology
MiddlesexUniversity
London
UK
ISSN 1860-949X ISSN 1860-9503 (electronic)
Studies inComputational Intelligence
ISBN 978-3-319-13825-1 ISBN 978-3-319-13826-8 (eBook)
DOI 10.1007/978-3-319-13826-8
LibraryofCongressControlNumber:2014956560
SpringerChamHeidelbergNewYorkDordrechtLondon
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Preface
In many applications, we have to deal with complex optimization problems with
complicated constraints. Such problems can be challenging to solve, due to their
complexity, nonlinearity and potentially high-dimensionality. These problems can
even be NP-hard, and thus require alternative solution methods because conven-
tional methods usually cannot deal with such complex problems. In recent years,
nature-inspiredmetaheuristic algorithms havegainedhugepopularitybecausethey
have demonstrated some promising results in solve tough optimization problems.
These metaheuristic algorithms include ant colony optimization, particle swarm
optimization, cuckoo search, firefly algorithm, bat algorithm, flower pollination
algorithm, bee algorithms and others. There are many reasons for such popularity.
In general, these algorithms tend to be flexible, efficient and highly adaptable, and
yeteasytoimplement.Thehighefficiencyofthesealgorithmsmakesitpossibleto
apply them to a wide range of problems in diverse applications.
Swarmintelligenceisquiteageneralconceptinthatmultipleagentsinteractand
exchange information, following simple rules. Rather surprisingly, such simple
systemscanshowcomplex,self-organizedbehaviour.Thoughthecharacteristicsof
agent interactions may be drawn from different sources of inspiration in nature,
algorithmicprocedurescanbequitesimpleandflexible,andyetefficientinpractice.
On the other hand, evolutionary computation is traditionally considered as part of
computational intelligence, which concerns optimization with continuous, combi-
natorialormixedproblems.Algorithmssuchasgeneticalgorithmsandevolutionary
strategy are good examples of evolutionary computation. However, evolutionary
computation hasbroadeneditsscope andextendedtoincludemanyareas. Loosely
speaking,swarmintelligenceispartoftheevolutionarycomputationparadigm,but
theinterestsinswarmintelligencearesooverwhelmingthatswarmintelligencehas
almostbecomeafieldbyitself.Here,wewillnotdebateonwhattherightterminology
orfieldsshouldbe.Wediscussbothswarmintelligenceandevolutionarycomputa-
tioninthebroadestsenseinthisbook.
The rapid advances in swarm intelligence and evolutionary computation have
resulted in a much richer literature. This timely review volume summarizes the
state-of-the-art developments in nature-inspired algorithms and applications with
v
vi Preface
the emphasis on swarm intelligence and bio-inspired computation. Topics include
the analysis and overview of swarm intelligence and evolutionary computation,
hybrid metaheuristic algorithms, bat algorithm, discrete cuckoo search, firefly
algorithm, particle swarm optimization, and harmony search as well as convergent
hybridization.Applicationcasestudieshavefocusedonthefeatureselectionbythe
binary flower pollination algorithm, dehydration of fruits and vegetables by the
firefly algorithm and goal programming, job shop scheduling, single row facility
layoutoptimization,trainingoffeed-forwardneuralnetworks,damageandstiffness
identification, synthesis of cross-ambiguity functions by the bat algorithm, web
document clustering, truss analysis, water distribution networks, sustainable
building designs and others.
As a timely review, this book can serve as an ideal reference for graduates,
lecturers, engineers and researchers in computer science, evolutionary computing,
artificial intelligence, machine learning, computational intelligence, data mining,
engineering optimization and designs.
I would like to thank our editors, Drs. Thomas Ditzinger and Holger Schaepe,
and the staff at Springer for their help and professionalism. Last but not least,
I thank my family for the help and support.
London, September 2014 Xin-She Yang
Contents
Swarm Intelligence and Evolutionary Computation: Overview
and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Xin-She Yang and Xingshi He
Globally Convergent Hybridization of Particle Swarm Optimization
Using Line Search-Based Derivative-Free Techniques . . . . . . . . . . . . . 25
A. Serani, M. Diez, E.F. Campana, G. Fasano, D. Peri and U. Iemma
Fireflies in the Fruits and Vegetables: Combining the Firefly
Algorithm with Goal Programming for Setting Optimal
Osmotic Dehydration Parameters of Produce . . . . . . . . . . . . . . . . . . . 49
Raha Imanirad and Julian Scott Yeomans
Hybrid Metaheuristic Algorithms: Past, Present, and Future. . . . . . . . 71
T.O. Ting, Xin-She Yang, Shi Cheng and Kaizhu Huang
Binary Flower Pollination Algorithm and Its Application
to Feature Selection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
Douglas Rodrigues, Xin-She Yang, André Nunes de Souza
and João Paulo Papa
Bat Algorithm Application for the Single Row Facility Layout
Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
Sinem Büyüksaatçı
Discrete Cuckoo Search Applied to Job Shop Scheduling
Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
Aziz Ouaarab, Belaïd Ahiod and Xin-She Yang
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viii Contents
Cuckoo Search and Bat Algorithm Applied to Training
Feed-Forward Neural Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
Milan Tuba, Adis Alihodzic and Nebojsa Bacanin
The Potential of the Firefly Algorithm for Damage Localization
and Stiffness Identification. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
Sara Casciati and Lorenzo Elia
Synthesizing Cross-Ambiguity Functions Using the Improved
Bat Algorithm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179
Momin Jamil, Hans-Jürgen Zepernick and Xin-She Yang
Sustainable Building Design: A Review on Recent Metaheuristic
Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203
Somayeh Asadi and Zong Woo Geem
Firefly Algorithm for Flow Shop Optimization . . . . . . . . . . . . . . . . . . 225
M.K. Marichelvam, T. Prabaharan and M. Geetha
Evaluation of Harmony Search and Differential Evolution
Optimization Algorithms on Solving the Booster Station
Optimization Problems in Water Distribution Networks . . . . . . . . . . . 245
Şerife Gökçe and M. Tamer Ayvaz
Web Document Clustering by Using PSO-Based Cuckoo
Search Clustering Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263
Moe Moe Zaw and Ei Ei Mon
Geometrically Nonlinear Analysis of Trusses Using Particle
Swarm Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283
Rasim Temür, Yusuf Sait Türkan and Yusuf Cengiz Toklu
Contributors
Belaïd Ahiod LRIT, Associated Unit to the CNRST (URAC 29), Mohammed
V-Agdal University, Rabat, Morocco
Adis Alihodzic University of Sarajevo, Sarajevo, Bosnia and Herzegovina
Somayeh Asadi Department of Architectural Engineering, Pennsylvania State
University, University Park, PA, USA
M. Tamer Ayvaz Department of Civil Engineering, Pamukkale University,
Denizli, Turkey
Nebojsa Bacanin Megatrend University Belgrade, Belgrade, Serbia
Sinem Büyüksaatçı Faculty of Engineering, Department of Industrial Engineer-
ing, Istanbul University, İstanbul, Turkey
E.F. Campana CNR-INSEAN, Rome, Italy
Sara Casciati Department of Civil Engineering and Architecture, University of
Catania, Siracusa, Italy
ShiCheng DivisionofComputerScience,TheUniversityofNottingham,Ningbo,
Zhejiang Province, China
André Nunes de Souza Department of Electrical Engineering, UNESP, Bauru,
SP, Brazil
M. Diez CNR-INSEAN, Rome, Italy
Lorenzo Elia Department of Civil Engineering and Architecture, University
of Pavia, Pavia, Italy
G.Fasano DepartmentofManagement,UniversityCa’FoscariofVenice,Venice,
Italy
ZongWooGeem DepartmentofEnergyIT,GachonUniversity,Seongnam,South
Korea
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