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Reviews the literature of the Moth-Flame Optimization algorithm;
Provides an in-depth analysis of equations, mathematical models,
and mechanisms of the Moth-Flame Optimization algorithm; Proposes
different variants of the Moth-Flame Optimization algorithm to
solve binary, multi-objective, noisy, dynamic, and combinatorial
optimization problems; Demonstrates how to design, develop, and
test different hybrids of Moth-Flame Optimization algorithm;
Introduces several applications areas of the Moth-Flame
Optimization algorithm focusing in sustainability.
This book presents select papers from 3rd EAI International
Conference on Computational Intelligence and Communications (CICom
2022). The papers reveal recent advances in the broader domains of
Computational Intelligence including (1) automation, control, and
intelligent transportation system, (2) big data, internet of
things, and smart cities, (3) wireless communication systems and
cyber security and (4) human/brain-computer interfaces, and image
and pattern recognition. The book demonstrates complex real-world
problems in which mathematical or traditional modellings are not
the preferred solution, hence alternative solutions are needed.
This collection of applications demonstrates the important advances
in computational intelligence. The book chapters’ present various
ideas that will benefit researchers, graduate students and
engineers in this domain.
This book provides an in-depth analysis of the current evolutionary
clustering techniques. It discusses the most highly regarded
methods for data clustering. The book provides literature reviews
about single objective and multi-objective evolutionary clustering
algorithms. In addition, the book provides a comprehensive review
of the fitness functions and evaluation measures that are used in
most of evolutionary clustering algorithms. Furthermore, it
provides a conceptual analysis including definition, validation and
quality measures, applications, and implementations for data
clustering using classical and modern nature-inspired techniques.
It features a range of proven and recent nature-inspired algorithms
used to data clustering, including particle swarm optimization, ant
colony optimization, grey wolf optimizer, salp swarm algorithm,
multi-verse optimizer, Harris hawks optimization, beta-hill
climbing optimization. The book also covers applications of
evolutionary data clustering in diverse fields such as image
segmentation, medical applications, and pavement infrastructure
asset management.
This book provides an in-depth analysis of the current evolutionary
clustering techniques. It discusses the most highly regarded
methods for data clustering. The book provides literature reviews
about single objective and multi-objective evolutionary clustering
algorithms. In addition, the book provides a comprehensive review
of the fitness functions and evaluation measures that are used in
most of evolutionary clustering algorithms. Furthermore, it
provides a conceptual analysis including definition, validation and
quality measures, applications, and implementations for data
clustering using classical and modern nature-inspired techniques.
It features a range of proven and recent nature-inspired algorithms
used to data clustering, including particle swarm optimization, ant
colony optimization, grey wolf optimizer, salp swarm algorithm,
multi-verse optimizer, Harris hawks optimization, beta-hill
climbing optimization. The book also covers applications of
evolutionary data clustering in diverse fields such as image
segmentation, medical applications, and pavement infrastructure
asset management.
This book provides an in-depth analysis of the current evolutionary
machine learning techniques. Discussing the most highly regarded
methods for classification, clustering, regression, and prediction,
it includes techniques such as support vector machines, extreme
learning machines, evolutionary feature selection, artificial
neural networks including feed-forward neural networks, multi-layer
perceptron, probabilistic neural networks, self-optimizing neural
networks, radial basis function networks, recurrent neural
networks, spiking neural networks, neuro-fuzzy networks, modular
neural networks, physical neural networks, and deep neural
networks. The book provides essential definitions, literature
reviews, and the training algorithms for machine learning using
classical and modern nature-inspired techniques. It also
investigates the pros and cons of classical training algorithms. It
features a range of proven and recent nature-inspired algorithms
used to train different types of artificial neural networks,
including genetic algorithm, ant colony optimization, particle
swarm optimization, grey wolf optimizer, whale optimization
algorithm, ant lion optimizer, moth flame algorithm, dragonfly
algorithm, salp swarm algorithm, multi-verse optimizer, and sine
cosine algorithm. The book also covers applications of the improved
artificial neural networks to solve classification, clustering,
prediction and regression problems in diverse fields.
This book covers the conventional and most recent theories and
applications in the area of evolutionary algorithms, swarm
intelligence, and meta-heuristics. Each chapter offers a
comprehensive description of a specific algorithm, from the
mathematical model to its practical application. Different kind of
optimization problems are solved in this book, including those
related to path planning, image processing, hand gesture detection,
among others. All in all, the book offers a tutorial on how to
design, adapt, and evaluate evolutionary algorithms. Source codes
for most of the proposed techniques have been included as
supplementary materials on a dedicated webpage.
This book provides an in-depth analysis of the current evolutionary
machine learning techniques. Discussing the most highly regarded
methods for classification, clustering, regression, and prediction,
it includes techniques such as support vector machines, extreme
learning machines, evolutionary feature selection, artificial
neural networks including feed-forward neural networks, multi-layer
perceptron, probabilistic neural networks, self-optimizing neural
networks, radial basis function networks, recurrent neural
networks, spiking neural networks, neuro-fuzzy networks, modular
neural networks, physical neural networks, and deep neural
networks. The book provides essential definitions, literature
reviews, and the training algorithms for machine learning using
classical and modern nature-inspired techniques. It also
investigates the pros and cons of classical training algorithms. It
features a range of proven and recent nature-inspired algorithms
used to train different types of artificial neural networks,
including genetic algorithm, ant colony optimization, particle
swarm optimization, grey wolf optimizer, whale optimization
algorithm, ant lion optimizer, moth flame algorithm, dragonfly
algorithm, salp swarm algorithm, multi-verse optimizer, and sine
cosine algorithm. The book also covers applications of the improved
artificial neural networks to solve classification, clustering,
prediction and regression problems in diverse fields.
This book focuses on the most well-regarded and recent
nature-inspired algorithms capable of solving optimization problems
with multiple objectives. Firstly, it provides preliminaries and
essential definitions in multi-objective problems and different
paradigms to solve them. It then presents an in-depth explanations
of the theory, literature review, and applications of several
widely-used algorithms, such as Multi-objective Particle Swarm
Optimizer, Multi-Objective Genetic Algorithm and Multi-objective
GreyWolf Optimizer Due to the simplicity of the techniques and
flexibility, readers from any field of study can employ them for
solving multi-objective optimization problem. The book provides the
source codes for all the proposed algorithms on a dedicated
webpage.
This book covers the conventional and most recent theories and
applications in the area of evolutionary algorithms, swarm
intelligence, and meta-heuristics. Each chapter offers a
comprehensive description of a specific algorithm, from the
mathematical model to its practical application. Different kind of
optimization problems are solved in this book, including those
related to path planning, image processing, hand gesture detection,
among others. All in all, the book offers a tutorial on how to
design, adapt, and evaluate evolutionary algorithms. Source codes
for most of the proposed techniques have been included as
supplementary materials on a dedicated webpage.
Handbook of Whale Optimization Algorithm: Variants, Hybrids,
Improvements, and Applications provides the most in-depth look at
an emerging meta-heuristic that has been widely used in both
science and industry. Whale Optimization Algorithm has been cited
more than 5000 times in Google Scholar, thus solving optimization
problems using this algorithm requires addressing a number of
challenges including multiple objectives, constraints, binary
decision variables, large-scale search space, dynamic objective
function, and noisy parameters to name a few. This handbook
provides readers with in-depth analysis of this algorithm and
existing methods in the literature to cope with such challenges.
The authors and editors also propose several improvements, variants
and hybrids of this algorithm. Several applications are also
covered to demonstrate the applicability of methods in this book.
This book introduces readers to the fundamentals of artificial
neural networks, with a special emphasis on evolutionary
algorithms. At first, the book offers a literature review of
several well-regarded evolutionary algorithms, including particle
swarm and ant colony optimization, genetic algorithms and
biogeography-based optimization. It then proposes evolutionary
version of several types of neural networks such as feed forward
neural networks, radial basis function networks, as well as
recurrent neural networks and multi-later perceptron. Most of the
challenges that have to be addressed when training artificial
neural networks using evolutionary algorithms are discussed in
detail. The book also demonstrates the application of the proposed
algorithms for several purposes such as classification, clustering,
approximation, and prediction problems. It provides a tutorial on
how to design, adapt, and evaluate artificial neural networks as
well, and includes source codes for most of the proposed techniques
as supplementary materials.
Swarm Intelligence (SI) has grown significantly, both from the
perspective of algorithmic development and applications covering
almost all disciplines science and technology. This book emphasizes
the studies of existing SI techniques, their variants and
applications. The book also contains reviews of new developments in
SI techniques and hybridizations. Algorithm specific studies
covering basic introduction and analysis of key components of these
algorithms, such as convergence, balance of solution accuracy,
computational costs, tuning and control of parameters. Application
specific studies incorporating the ways of designing objective
functions, solution representation and constraint handling. The
book also includes studies on application domain specific
adaptations in the SI techniques. The book will be beneficial for
academicians and researchers from various disciplines of
engineering and science working in applications of SI and other
optimization problems.
This book introduces readers to the fundamentals of artificial
neural networks, with a special emphasis on evolutionary
algorithms. At first, the book offers a literature review of
several well-regarded evolutionary algorithms, including particle
swarm and ant colony optimization, genetic algorithms and
biogeography-based optimization. It then proposes evolutionary
version of several types of neural networks such as feed forward
neural networks, radial basis function networks, as well as
recurrent neural networks and multi-later perceptron. Most of the
challenges that have to be addressed when training artificial
neural networks using evolutionary algorithms are discussed in
detail. The book also demonstrates the application of the proposed
algorithms for several purposes such as classification, clustering,
approximation, and prediction problems. It provides a tutorial on
how to design, adapt, and evaluate artificial neural networks as
well, and includes source codes for most of the proposed techniques
as supplementary materials.
This book is a collection of best selected research papers
presented at the International Conference on Communication and
Artificial Intelligence (ICCAI 2021), held in the Department of
Electronics & Communication Engineering, GLA University,
Mathura, India, during 19-20 November 2021. The primary focus of
the book is on the research information related to artificial
intelligence, networks, and smart systems applied in the areas of
industries, government sectors, and educational institutions
worldwide. Diverse themes with a central idea of sustainable
networking solutions are discussed in the book. The book presents
innovative work by leading academics, researchers, and experts from
industry.
This book comprises the proceedings of the International Conference
on Intelligent Systems and Applications (ICISA 2022). The contents
of this volume focus on novel and modified artificial intelligence
and machine learning-based methods and their applications in
robotics, pharmaceutics, banking & finance, agriculture, food
processing, crime prevention, smart homes, transportation, traffic
control, and wildlife conservation, etc. This volume will prove a
valuable resource for those in academia and industry.
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