|
Showing 1 - 23 of
23 matches in All Departments
Metaheuristic algorithms are considered as generic optimization
tools that can solve very complex problems characterized by having
very large search spaces. Metaheuristic methods reduce the
effective size of the search space through the use of effective
search strategies. Book Features: Provides a unified view of the
most popular metaheuristic methods currently in use Includes the
necessary concepts to enable readers to implement and modify
already known metaheuristic methods to solve problems Covers design
aspects and implementation in MATLAB (R) Contains numerous examples
of problems and solutions that demonstrate the power of these
methods of optimization The material has been written from a
teaching perspective and, for this reason, this book is primarily
intended for undergraduate and postgraduate students of artificial
intelligence, metaheuristic methods, and/or evolutionary
computation. The objective is to bridge the gap between
metaheuristic techniques and complex optimization problems that
profit from the convenient properties of metaheuristic approaches.
Therefore, engineer practitioners who are not familiar with
metaheuristic computation will appreciate that the techniques
discussed are beyond simple theoretical tools, since they have been
adapted to solve significant problems that commonly arise in such
areas.
Metaheuristic algorithms are considered as generic optimization
tools that can solve very complex problems characterized by having
very large search spaces. Metaheuristic methods reduce the
effective size of the search space through the use of effective
search strategies. Book Features: Provides a unified view of the
most popular metaheuristic methods currently in use Includes the
necessary concepts to enable readers to implement and modify
already known metaheuristic methods to solve problems Covers design
aspects and implementation in MATLAB (R) Contains numerous examples
of problems and solutions that demonstrate the power of these
methods of optimization The material has been written from a
teaching perspective and, for this reason, this book is primarily
intended for undergraduate and postgraduate students of artificial
intelligence, metaheuristic methods, and/or evolutionary
computation. The objective is to bridge the gap between
metaheuristic techniques and complex optimization problems that
profit from the convenient properties of metaheuristic approaches.
Therefore, engineer practitioners who are not familiar with
metaheuristic computation will appreciate that the techniques
discussed are beyond simple theoretical tools, since they have been
adapted to solve significant problems that commonly arise in such
areas.
This book is primarily intended for undergraduate and postgraduate
students of Science, Electrical Engineering, or Computational
Mathematics. Metaheuristic search methods are so numerous and
varied in terms of design and potential applications; however, for
such an abundant family of optimization techniques, there seems to
be a question which needs to be answered: Which part of the design
in a metaheuristic algorithm contributes more to its better
performance? Several works that compare the performance among
metaheuristic approaches have been reported in the literature.
Nevertheless, they suffer from one of the following limitations:
(A)Their conclusions are based on the performance of popular
evolutionary approaches over a set of synthetic functions with
exact solutions and well-known behaviors, without considering the
application context or including recent developments. (B) Their
conclusions consider only the comparison of their final results
which cannot evaluate the nature of a good or bad balance between
exploration and exploitation. The objective of this book is to
compare the performance of various metaheuristic techniques when
they are faced with complex optimization problems extracted from
different engineering domains. The material has been compiled from
a teaching perspective.
This book includes two objectives. The first goal is to present
advances and developments which have proved to be effective in
their application to several complex problems. The second objective
is to present the performance comparison of various metaheuristic
techniques when they face complex optimization problems. The
material has been compiled from a teaching perspective. Most of the
problems in science, engineering, economics, and other areas can be
translated as an optimization or a search problem. According to
their characteristics, some problems can be simple that can be
solved by traditional optimization methods based on mathematical
analysis. However, most of the problems of practical importance in
engineering represent complex scenarios so that they are very hard
to be solved by using traditional approaches. Under such
circumstances, metaheuristic has emerged as the best alternative to
solve this kind of complex formulations. This book is primarily
intended for undergraduate and postgraduate students. Engineers and
application developers can also benefit from the book contents
since it has been structured so that each chapter can be read
independently from the others, and therefore, only potential
interesting information can be quickly available for solving an
industrial problem at hand.
This book is primarily intended for undergraduate and postgraduate
students of Science, Electrical Engineering, or Computational
Mathematics. Metaheuristic search methods are so numerous and
varied in terms of design and potential applications; however, for
such an abundant family of optimization techniques, there seems to
be a question which needs to be answered: Which part of the design
in a metaheuristic algorithm contributes more to its better
performance? Several works that compare the performance among
metaheuristic approaches have been reported in the literature.
Nevertheless, they suffer from one of the following limitations:
(A)Their conclusions are based on the performance of popular
evolutionary approaches over a set of synthetic functions with
exact solutions and well-known behaviors, without considering the
application context or including recent developments. (B) Their
conclusions consider only the comparison of their final results
which cannot evaluate the nature of a good or bad balance between
exploration and exploitation. The objective of this book is to
compare the performance of various metaheuristic techniques when
they are faced with complex optimization problems extracted from
different engineering domains. The material has been compiled from
a teaching perspective.
This book includes two objectives. The first goal is to present
advances and developments which have proved to be effective in
their application to several complex problems. The second objective
is to present the performance comparison of various metaheuristic
techniques when they face complex optimization problems. The
material has been compiled from a teaching perspective. Most of the
problems in science, engineering, economics, and other areas can be
translated as an optimization or a search problem. According to
their characteristics, some problems can be simple that can be
solved by traditional optimization methods based on mathematical
analysis. However, most of the problems of practical importance in
engineering represent complex scenarios so that they are very hard
to be solved by using traditional approaches. Under such
circumstances, metaheuristic has emerged as the best alternative to
solve this kind of complex formulations. This book is primarily
intended for undergraduate and postgraduate students. Engineers and
application developers can also benefit from the book contents
since it has been structured so that each chapter can be read
independently from the others, and therefore, only potential
interesting information can be quickly available for solving an
industrial problem at hand.
This book presents new, alternative metaheuristic developments that
have proved to be effective in various complex problems to help
researchers, lecturers, engineers, and practitioners solve their
own optimization problems. It also bridges the gap between recent
metaheuristic techniques and interesting identification system
methods that benefit from the convenience of metaheuristic schemes
by explaining basic ideas of the proposed applications in ways that
can be understood by readers new to these fields. As such it is a
valuable resource for energy practitioners who are not researchers
in metaheuristics. In addition, it offers members of the
metaheuristic community insights into how system identification and
energy problems can be translated into optimization tasks.
This book presents new, alternative metaheuristic developments that
have proved to be effective in various complex problems to help
researchers, lecturers, engineers, and practitioners solve their
own optimization problems. It also bridges the gap between recent
metaheuristic techniques and interesting identification system
methods that benefit from the convenience of metaheuristic schemes
by explaining basic ideas of the proposed applications in ways that
can be understood by readers new to these fields. As such it is a
valuable resource for energy practitioners who are not researchers
in metaheuristics. In addition, it offers members of the
metaheuristic community insights into how system identification and
energy problems can be translated into optimization tasks.
This book presents advances in alternative swarm development that
have proved to be effective in several complex problems. Swarm
intelligence (SI) is a problem-solving methodology that results
from the cooperation between a set of agents with similar
characteristics. The study of biological entities, such as animals
and insects, manifesting social behavior has resulted in several
computational models of swarm intelligence. While there are
numerous books addressing the most widely known swarm methods,
namely ant colony algorithms and particle swarm optimization, those
discussing new alternative approaches are rare. The focus on
developments based on the simple modification of popular swarm
methods overlooks the opportunity to discover new techniques and
procedures that can be useful in solving problems formulated by the
academic and industrial communities. Presenting various novel swarm
methods and their practical applications, the book helps
researchers, lecturers, engineers and practitioners solve their own
optimization problems.
This book discusses the use of efficient metaheuristic algorithms
to solve diverse power system problems, providing an overview of
the various aspects of metaheuristic methods to enable readers to
gain a comprehensive understanding of the field and of conducting
studies on specific metaheuristic algorithms related to
power-system applications. By bridging the gap between recent
metaheuristic techniques and novel power system methods that
benefit from the convenience of metaheuristic methods, it offers
power system practitioners who are not metaheuristic computation
researchers insights into the techniques, which go beyond simple
theoretical tools and have been adapted to solve important problems
that commonly arise. On the other hand, members of the
metaheuristic computation community learn how power engineering
problems can be translated into optimization tasks, and it is also
of interest to engineers and application developers. Further, since
each chapter can be read independently, the relevant information
can be quickly found. Power systems is a multidisciplinary field
that addresses the multiple approaches used for design and analysis
in areas ranging from signal processing, and electronics to
computational intelligence, including the current trend of
metaheuristic computation.
This book presents a study of the use of optimization algorithms in
complex image processing problems. The problems selected explore
areas ranging from the theory of image segmentation to the
detection of complex objects in medical images. Furthermore, the
concepts of machine learning and optimization are analyzed to
provide an overview of the application of these tools in image
processing. The material has been compiled from a teaching
perspective. Accordingly, the book is primarily intended for
undergraduate and postgraduate students of Science, Engineering,
and Computational Mathematics, and can be used for courses on
Artificial Intelligence, Advanced Image Processing, Computational
Intelligence, etc. Likewise, the material can be useful for
research from the evolutionary computation, artificial intelligence
and image processing communities.
This book compares the performance of various evolutionary
computation (EC) techniques when they are faced with complex
optimization problems extracted from different engineering domains.
Particularly focusing on recently developed algorithms, it is
designed so that each chapter can be read independently. Several
comparisons among EC techniques have been reported in the
literature, however, they all suffer from one limitation: their
conclusions are based on the performance of popular evolutionary
approaches over a set of synthetic functions with exact solutions
and well-known behaviors, without considering the application
context or including recent developments. In each chapter, a
complex engineering optimization problem is posed, and then a
particular EC technique is presented as the best choice, according
to its search characteristics. Lastly, a set of experiments is
conducted in order to compare its performance to other popular EC
methods.
This book bridges the gap between Soft Computing techniques and
their applications to complex engineering problems. In each chapter
we endeavor to explain the basic ideas behind the proposed
applications in an accessible format for readers who may not
possess a background in some of the fields. Therefore, engineers or
practitioners who are not familiar with Soft Computing methods will
appreciate that the techniques discussed go beyond simple
theoretical tools, since they have been adapted to solve
significant problems that commonly arise in such areas. At the same
time, the book will show members of the Soft Computing community
how engineering problems are now being solved and handled with the
help of intelligent approaches. Highlighting new applications and
implementations of Soft Computing approaches in various engineering
contexts, the book is divided into 12 chapters. Further, it has
been structured so that each chapter can be read independently of
the others.
The goal of this book is to present advances that discuss
alternative Evolutionary Computation (EC) developments and
non-conventional operators which have proved to be effective in the
solution of several complex problems. The book has been structured
so that each chapter can be read independently from the others. The
book contains nine chapters with the following themes: 1)
Introduction, 2) the Social Spider Optimization (SSO), 3) the
States of Matter Search (SMS), 4) the collective animal behavior
(CAB) algorithm, 5) the Allostatic Optimization (AO) method, 6) the
Locust Search (LS) algorithm, 7) the Adaptive Population with
Reduced Evaluations (APRE) method, 8) the multimodal CAB, 9) the
constrained SSO method.
This book explores new alternative metaheuristic developments that
have proved to be effective in their application to several complex
problems. Though most of the new metaheuristic algorithms
considered offer promising results, they are nevertheless still in
their infancy. To grow and attain their full potential, new
metaheuristic methods must be applied in a great variety of
problems and contexts, so that they not only perform well in their
reported sets of optimization problems, but also in new complex
formulations. The only way to accomplish this is to disseminate
these methods in various technical areas as optimization tools. In
general, once a scientist, engineer or practitioner recognizes a
problem as a particular instance of a more generic class, he/she
can select one of several metaheuristic algorithms that guarantee
an expected optimization performance. Unfortunately, the set of
options are concentrated on algorithms whose popularity and high
proliferation outstrip those of the new developments. This
structure is important, because the authors recognize this
methodology as the best way to help researchers, lecturers,
engineers and practitioners solve their own optimization problems.
This book compares the performance of various evolutionary
computation (EC) techniques when they are faced with complex
optimization problems extracted from different engineering domains.
Particularly focusing on recently developed algorithms, it is
designed so that each chapter can be read independently. Several
comparisons among EC techniques have been reported in the
literature, however, they all suffer from one limitation: their
conclusions are based on the performance of popular evolutionary
approaches over a set of synthetic functions with exact solutions
and well-known behaviors, without considering the application
context or including recent developments. In each chapter, a
complex engineering optimization problem is posed, and then a
particular EC technique is presented as the best choice, according
to its search characteristics. Lastly, a set of experiments is
conducted in order to compare its performance to other popular EC
methods.
This book presents a study of the use of optimization algorithms in
complex image processing problems. The problems selected explore
areas ranging from the theory of image segmentation to the
detection of complex objects in medical images. Furthermore, the
concepts of machine learning and optimization are analyzed to
provide an overview of the application of these tools in image
processing. The material has been compiled from a teaching
perspective. Accordingly, the book is primarily intended for
undergraduate and postgraduate students of Science, Engineering,
and Computational Mathematics, and can be used for courses on
Artificial Intelligence, Advanced Image Processing, Computational
Intelligence, etc. Likewise, the material can be useful for
research from the evolutionary computation, artificial intelligence
and image processing communities.
This book bridges the gap between Soft Computing techniques and
their applications to complex engineering problems. In each chapter
we endeavor to explain the basic ideas behind the proposed
applications in an accessible format for readers who may not
possess a background in some of the fields. Therefore, engineers or
practitioners who are not familiar with Soft Computing methods will
appreciate that the techniques discussed go beyond simple
theoretical tools, since they have been adapted to solve
significant problems that commonly arise in such areas. At the same
time, the book will show members of the Soft Computing community
how engineering problems are now being solved and handled with the
help of intelligent approaches. Highlighting new applications and
implementations of Soft Computing approaches in various engineering
contexts, the book is divided into 12 chapters. Further, it has
been structured so that each chapter can be read independently of
the others.
This book presents the use of efficient Evolutionary Computation
(EC) algorithms for solving diverse real-world image processing and
pattern recognition problems. It provides an overview of the
different aspects of evolutionary methods in order to enable the
reader in reaching a global understanding of the field and, in
conducting studies on specific evolutionary techniques that are
related to applications in image processing and pattern
recognition. It explains the basic ideas of the proposed
applications in a way that can also be understood by readers
outside of the field. Image processing and pattern recognition
practitioners who are not evolutionary computation researchers will
appreciate the discussed techniques beyond simple theoretical tools
since they have been adapted to solve significant problems that
commonly arise on such areas. On the other hand, members of the
evolutionary computation community can learn the way in which image
processing and pattern recognition problems can be translated into
an optimization task. The book has been structured so that each
chapter can be read independently from the others. It can serve as
reference book for students and researchers with basic knowledge in
image processing and EC methods.
The goal of this book is to present advances that discuss
alternative Evolutionary Computation (EC) developments and
non-conventional operators which have proved to be effective in the
solution of several complex problems. The book has been structured
so that each chapter can be read independently from the others. The
book contains nine chapters with the following themes: 1)
Introduction, 2) the Social Spider Optimization (SSO), 3) the
States of Matter Search (SMS), 4) the collective animal behavior
(CAB) algorithm, 5) the Allostatic Optimization (AO) method, 6) the
Locust Search (LS) algorithm, 7) the Adaptive Population with
Reduced Evaluations (APRE) method, 8) the multimodal CAB, 9) the
constrained SSO method.
This book presents the use of efficient Evolutionary Computation
(EC) algorithms for solving diverse real-world image processing and
pattern recognition problems. It provides an overview of the
different aspects of evolutionary methods in order to enable the
reader in reaching a global understanding of the field and, in
conducting studies on specific evolutionary techniques that are
related to applications in image processing and pattern
recognition. It explains the basic ideas of the proposed
applications in a way that can also be understood by readers
outside of the field. Image processing and pattern recognition
practitioners who are not evolutionary computation researchers will
appreciate the discussed techniques beyond simple theoretical tools
since they have been adapted to solve significant problems that
commonly arise on such areas. On the other hand, members of the
evolutionary computation community can learn the way in which image
processing and pattern recognition problems can be translated into
an optimization task. The book has been structured so that each
chapter can be read independently from the others. It can serve as
reference book for students and researchers with basic knowledge in
image processing and EC methods.
This book presents a comparative perspective of current
metaheuristic developments, which have proved to be effective in
their application to several complex problems. The study of
biological and social entities such as animals, humans, or insects
that manifest a cooperative behavior has produced several
computational models in metaheuristic methods. Although these
schemes emulate very different processes or systems, the rules used
to model individual behavior are very similar. Under such
conditions, it is not clear to identify which are the advantages or
disadvantages of each metaheuristic technique. The book is compiled
from a teaching perspective. For this reason, the book is primarily
intended for undergraduate and postgraduate students of Science,
Electrical Engineering, or Computational Mathematics. It is
appropriate for courses such as Artificial Intelligence, Electrical
Engineering, Evolutionary Computation. The book is also useful for
researchers from the evolutionary and engineering communities.
Likewise, engineer practitioners, who are not familiar with
metaheuristic computation concepts, will appreciate that the
techniques discussed are beyond simple theoretical tools since they
have been adapted to solve significant problems that commonly arise
in engineering areas.
|
You may like...
Poldark: Series 1-2
Aidan Turner, Eleanor Tomlinson, …
Blu-ray disc
(1)
R55
Discovery Miles 550
Hampstead
Diane Keaton, Brendan Gleeson, …
DVD
R49
Discovery Miles 490
|