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Intelligent Systems Reference Library 62
Rough Guide to 134
Intelligent Systems Reference Library
Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
Lakhmi C. Jain, University of Canberra, Canberra, Australia
For further volumes:
About this Series
The aim of this series is to publish a Reference Library, including novel advances
and developments in all aspects of Intelligent Systems in an easily accessible and
well structured form. The series includes reference works, handbooks, compendia,
textbooks, well-structured monographs, dictionaries, and encyclopedias. It contains well integrated knowledge and current information in the field of Intelligent
Systems. The series covers the theory, applications, and design methods of
Intelligent Systems. Virtually all disciplines such as engineering, computer science, avionics, business, e-commerce, environment, healthcare, physics and life
science are included.
Bo Xing Wen-Jing Gao
Intelligence: A Rough
Guide to 134 Clever
Department of Mechanical and Aeronautical
University of Pretoria
Department of New Product Development
Meiyuan Mould Design and Manufacturing
People’s Republic of China
ISSN 1868-4408 (electronic)
ISBN 978-3-319-03404-1 (eBook)
Springer Cham Heidelberg New York Dordrecht London
Library of Congress Control Number: 2013953686
Springer International Publishing Switzerland 2014
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Computational intelligence (CI) is a relatively new discipline, and accordingly,
there is little agreement about its precise definition. Nevertheless, most academicians and practitioners would include techniques such as artificial neural
network, fuzzy systems, many versions of evolutionary algorithms (e.g. evolution
strategies, genetic algorithm, genetic programming, differential evolution), as
well as ant colony optimization, artificial immune systems, multi-agent systems,
particle swarm optimization, and the hybridization versions of these, under the
umbrella of CI.
In contrast to this common trend, Bo and Wen-Jing offer us a brand new
perspective in the field of CI research through their book entitled Innovative
Computational Intelligence: A Rough Guide to 134 Clever Algorithms. This book
is unique because it contains in one source an overview of a wide range of newly
developed CI algorithms that are normally found in scattered resources. The
authors succeed in identifying this vast amount of novel CI algorithms and
grouping them into four large classes, namely, biology-, physics-, chemistry-, and
mathematics-based CI algorithms. Furthermore, the organization of the book is
such that each algorithm covered in the book contains the corresponding core
working principles and some preliminary performance evaluations. This style
would, no doubt, lead to the further development of these fascinating algorithms.
This book will be beneficial to a broad audience: First, university students,
particularly those pursuing their postgraduate studies in advanced subjects; Second, the algorithms introduced in this book can serve as foundations for
researchers to build bodies of knowledge in the fast growing area of CI research;
Finally, practitioners can also use the algorithms presented in this book to solve
and analyze specific real-world problems. Overall, this book makes a worthwhile
read and is a welcome edition to the CI literature.
Adelaide, Australia, September 2013
Computational intelligence (CI) is a fast evolving area in which many novel
algorithms, stemmed from various inspiring sources, were developed during the
past decade. Nevertheless, many of them are dispersed in different research
directions and their true potential is thus not fully utilized yet. Therefore, there is
an urgent need to have these newly developed CI algorithms compiled into one
single reference source.
Through over 1,630 non-repetitive supporting references, Bo and Wen-Jing
have made great efforts to respond to this requirement. In their book entitled
Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms,
the readers will enjoy their readings of a vast amount of novel CI algorithms which
have been carefully classified by Bo and Wen-Jing into four main groups, i.e.,
biology-, physics-, chemistry-, and mathematics-based CI algorithms. The four
parts of the book dedicated to these four groups of algorithms, respectively, are
independent of each other which also makes it an easy-to-use reference handbook.
The broad spectrum of articles collected in this monograph is a tribute to the
richness of the huge tree of CI research, which undoubtedly will continue to bear
fruit, develop offshoots, and shape new research directions in the near future. Thus
this book, to be published by Springer Intelligent Systems Reference Library Series,
should have a great appeal to graduate students, researchers, and practitioners.
Birmingham, UK October 2013
During the past decade, a number of new computational intelligence (CI) algorithms have been proposed. Unfortunately, they spread in a number of unrelated
publishing directions which may hamper the use of such published resources.
These provide us with motivation to analyze the existing research for categorizing
and synthesizing it in a meaningful manner. The mission of this book is really
important since those algorithms are going to be a new revolution in computer
science. We hope it will stimulate the readers to make novel contributions or to
even start a new paradigm based on nature phenomena. This book introduces 134
innovative CI algorithms. The book consists of 28 chapters which are organized as
five parts. Each part can be reviewed in any order and a brief description of each
individual chapter is provided as follows:
Part I Introduction
Chapter 1: In this chapter, we introduce some general knowledge relative to the
realm of CI. The desirable merits of these intelligent algorithms and their initial
successes in many domains have inspired researchers (from various backgrounds)
to continuously develop their successors. Such truly interdisciplinary environment
of the research and development provides more and more rewarding opportunities
for scientific breakthrough and technology innovation. We first introduce some
historical information regarding CI in Sect. 1.1. Then, the organizational structures
are detailed in Sect. 1.2. Finally, Sect. 1.3 summarizes this chapter.
Part II Biology-based CI Algorithms
Chapter 2: In this chapter, we present a set of algorithms that are inspired by the
different bacteria behavioral patterns, i.e., bacterial foraging algorithm (BFA),
bacterial colony chemotaxis (BCC) algorithm, superbug algorithm (SuA), bacterial
colony optimization (BCO) algorithm, and viral system (VS) algorithm. We first
describe the general knowledge of bacteria foraging behavior in Sect. 2.1. Then,
the fundamentals and performance of BFA, BCC algorithm, SuA, BCO algorithm,
and VS algorithm are introduced in Sects. 2.2 and 2.3, respectively. Finally,
Sect. 2.4 summarizes this chapter.
Chapter 3: In this chapter, we present two algorithms that are inspired by the
behaviors of bats, i.e., bat algorithm (BaA) and bat intelligence (BI) algorithm. We
first describe the general knowledge of the foraging behavior of bats in Sect. 3.1.
Then, the fundamentals and performance of the BaA and BI algorithm are introduced in Sects. 3.2 and 3.3, respectively. Finally, Sect. 3.4 summarizes this
Chapter 4: In this chapter, we present a set of algorithms that are inspired by
different honeybees behavioral patterns, i.e., artificial bee colony (ABC) algorithm,
honeybees mating optimization (HBMO) algorithm, artificial beehive algorithm
(ABHA), bee colony optimization (BCO) algorithm, bee colony inspired algorithm
(BCiA), bee swarm optimization (BSO) algorithm, bee system (BS) algorithm,
BeeHive algorithm, bees algorithm (BeA), bees life algorithm (BLA), bumblebees
algorithm, honeybee social foraging (HBSF) algorithm, OptBees algorithm,
simulated bee colony (SBC) algorithm, virtual bees algorithm (VBA), and wasp
swarm optimization (WSO) algorithm. We first describe the general knowledge
about honeybees in Sect. 4.1. Then, the fundamentals and performance of these
algorithms are introduced in Sects. 4.2–4.4, respectively. Finally, Sect. 4.5 summarizes this chapter.
Chapter 5: In this chapter, we introduce a novel optimization algorithm called
biogeography-based optimization (BBO) which is inspired by the science of
biogeography. We first describe the general knowledge about the science of biogeography in Sect. 5.1. Then, the fundamentals and performance of BBO are
introduced in Sect. 5.2. Finally, Sect. 5.3 summarizes this chapter.
Chapter 6: In this chapter, we present a new population-based method, called
cat swarm optimization (CSO) algorithm, which imitates the natural behavior of
cats. We first describe the general knowledge about the behavior of cats in
Sect. 6.1. Then, the fundamentals and performance of CSO are introduced in
Sect. 6.2. Next, some selected variations of CSO are explained in Sect. 6.3. Right
after this, Sect. 6.4 presents a representative CSO application. Finally, Sect. 6.5
summarizes this chapter.
Chapter 7: In this chapter, a set of cuckoo-inspired optimization algorithms, i.e.,
cuckoo search (CA) algorithm and cuckoo optimization algorithm (COA) are
introduced. We first, in Sect. 7.1, describe the general knowledge about cuckoos.
Then, the fundamentals and performance of CS are introduced in Sect. 7.2. Next,
the selected variants of CS are outlined in Sect. 7.3 which is followed by a
presentation of representative CS application in Sect. 7.4. Right after this, Sect. 7.5
introduces an emerging algorithm, i.e., COA, which also falls within this category.
Finally, Sect. 7.6 draws the conclusions of this chapter.
Chapter 8: In this chapter, we present three algorithms that are inspired by the
flashing behavior of luminous insects, i.e., firefly algorithm (FA), glowworm
swarm optimization (GlSO) algorithm, and bioluminescent swarm optimization