Swarm Intelligence for Resource Management in Internet of Things

Swarm Intelligence for Resource Management in Internet of Things Edited by
Aboul Ella Hassanien , Universitat Politècnica de Catalunya , Spain Ashraf
Darwish , Tunghai University , Taiwan Series Editor Fatos Xhafa Internet of
Things ( IoT ) ...

Swarm Intelligence for Resource Management in Internet of Things

Author: Aboul Ella Hassanien

Publisher: Academic Press

ISBN: 0128182881

Page: 192

View: 714

Internet of Things (IoT) is a new platform of various physical objects or “things equipped with sensors, electronics, smart devices, software, and network connections. IoT represents a new revolution of the Internet network which is driven by the recent advances of technologies such as sensor networks (wearable and implantable), mobile devices, networking, and cloud computing technologies. IoT permits these the smart devices to collect, store and analyze the collected data with limited storage and processing capacities. Swarm Intelligence for Resource Management in the Internet of Things presents a new approach in Artificial Intelligence that can be used for resources management in IoT, which is considered a critical issue for this network. The authors demonstrate these resource management applications using swarm intelligence techniques. Currently, IoT can be used in many important applications which include healthcare, smart cities, smart homes, smart hospitals, environment monitoring, and video surveillance. IoT devices cannot perform complex on-site data processing due to their limited battery and processing. However, the major processing unit of an application can be transmitted to other nodes, which are more powerful in terms of storage and processing. By applying swarm intelligence algorithms for IoT devices, we can provide major advantages for energy saving in IoT devices. Swarm Intelligence for Resource Management in the Internet of Things shows the reader how to overcome the problems and challenges of creating and implementing swarm intelligence algorithms for each application Examines the development and application of swarm intelligence systems in artificial intelligence as applied to the Internet of Things Discusses intelligent techniques for the implementation of swarm intelligence in IoT Prepared for researchers and specialists who are interested in the use and integration of IoT and cloud computing technologies

Swarm Intelligence

This book provides a detailed look at models of social insect behavior and how to apply these models in the design of complex systems.

Swarm Intelligence

Author: Eric Bonabeau

Publisher: Oxford University Press

ISBN: 9780198030157

Page: 320

View: 181

Social insects--ants, bees, termites, and wasps--can be viewed as powerful problem-solving systems with sophisticated collective intelligence. Composed of simple interacting agents, this intelligence lies in the networks of interactions among individuals and between individuals and the environment. A fascinating subject, social insects are also a powerful metaphor for artificial intelligence, and the problems they solve--finding food, dividing labor among nestmates, building nests, responding to external challenges--have important counterparts in engineering and computer science. This book provides a detailed look at models of social insect behavior and how to apply these models in the design of complex systems. The book shows how these models replace an emphasis on control, preprogramming, and centralization with designs featuring autonomy, emergence, and distributed functioning. These designs are proving immensely flexible and robust, able to adapt quickly to changing environments and to continue functioning even when individual elements fail. In particular, these designs are an exciting approach to the tremendous growth of complexity in software and information. Swarm Intelligence draws on up-to-date research from biology, neuroscience, artificial intelligence, robotics, operations research, and computer graphics, and each chapter is organized around a particular biological example, which is then used to develop an algorithm, a multiagent system, or a group of robots. The book will be an invaluable resource for a broad range of disciplines.

Advances in Swarm Intelligence Part I

The two-volume set (LNCS 6728 and 6729) constitutes the refereed proceedings of the International Conference on Swarm Intelligence, ICSI 2011, held in Chongqing, China, in June 2011.

Advances in Swarm Intelligence  Part I

Author: Ying Tan

Publisher: Springer Science & Business Media

ISBN: 3642215149

Page: 639

View: 855

The two-volume set (LNCS 6728 and 6729) constitutes the refereed proceedings of the International Conference on Swarm Intelligence, ICSI 2011, held in Chongqing, China, in June 2011. The 143 revised full papers presented were carefully reviewed and selected from 298 submissions. The papers are organized in topical sections on theoretical analysis of swarm intelligence algorithms, particle swarm optimization, applications of pso algorithms, ant colony optimization algorithms, bee colony algorithms, novel swarm-based optimization algorithms, artificial immune system, differential evolution, neural networks, genetic algorithms, evolutionary computation, fuzzy methods, and hybrid algorithms - for part I. Topics addressed in part II are such as multi-objective optimization algorithms, multi-robot, swarm-robot, and multi-agent systems, data mining methods, machine learning methods, feature selection algorithms, pattern recognition methods, intelligent control, other optimization algorithms and applications, data fusion and swarm intelligence, as well as fish school search - foundations and applications.

Swarm Intelligence and Bio Inspired Computation

In most of the MAs, randomization is realized using a uniform or Gaussian distribution. However, this is not the only way to achieve randomization.

Swarm Intelligence and Bio Inspired Computation

Author: Momin Jamil

Publisher: Elsevier Inc. Chapters

ISBN: 0128068892

Page: 450

View: 915

Random walks play an important and central role in metaheuristic and stochastic optimization algorithms. The two key components of the search process in metaheuristic algorithms (MAs) are intensification and diversification. The overall efficiency of a metaheuristic optimization algorithm depends on a sound balance between these two components. In MAs, exploration is achieved by randomization in combination with a deterministic procedure. In this way, the newly generated solutions are distributed as diversely as possible in the problem search space. In most of the MAs, randomization is realized using a uniform or Gaussian distribution. However, this is not the only way to achieve randomization. In recent years, the use of Lévy distribution has emerged as an alternative to uniform or Gaussian distributions. In view of these details, this chapter focuses on using Lévy flights (LFs) in the context of global optimization. A survey of the most important MAs using LFs to achieve intensification and diversification for solving global optimization problems is presented. The different components and concepts of Lévy-flight-based MAs are discussed and their similarities and differences are analyzed.

Swarm Intelligence

In addition to the introduction of new concepts of swarm intelligence, this book also presented some selected representative case studies covering power plant maintenance scheduling; geotechnical engineering; design and machining tolerances ...

Swarm Intelligence

Author: Felix Chan

Publisher: BoD – Books on Demand

ISBN: 3902613092

Page: 548

View: 274

In the era globalisation the emerging technologies are governing engineering industries to a multifaceted state. The escalating complexity has demanded researchers to find the possible ways of easing the solution of the problems. This has motivated the researchers to grasp ideas from the nature and implant it in the engineering sciences. This way of thinking led to emergence of many biologically inspired algorithms that have proven to be efficient in handling the computationally complex problems with competence such as Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), etc. Motivated by the capability of the biologically inspired algorithms the present book on "Swarm Intelligence: Focus on Ant and Particle Swarm Optimization" aims to present recent developments and applications concerning optimization with swarm intelligence techniques. The papers selected for this book comprise a cross-section of topics that reflect a variety of perspectives and disciplinary backgrounds. In addition to the introduction of new concepts of swarm intelligence, this book also presented some selected representative case studies covering power plant maintenance scheduling; geotechnical engineering; design and machining tolerances; layout problems; manufacturing process plan; job-shop scheduling; structural design; environmental dispatching problems; wireless communication; water distribution systems; multi-plant supply chain; fault diagnosis of airplane engines; and process scheduling. I believe these 27 chapters presented in this book adequately reflect these topics.

Swarm Intelligence

The surprising truth is that the future will be pioneered by the collective problem-solvers, making Swarm Intelligence a must-read for business leaders, educators, and anyone else concerned with nurturing creative intelligence and ...

Swarm Intelligence

Author: James Haywood Rolling, Jr.

Publisher: St. Martin's Press

ISBN: 1137401516

Page: 256

View: 893

Companies and organizations everywhere cite creativity as the most desirable - and elusive - leadership quality of the future. Yet scores measuring creativity among American children have been on the wane for decades. A specialist in creative leadership, professor James Haywood Rolling, Jr. knows firsthand that the classroom is a key to either unlocking or blocking the critical imagination. He argues that today's schools, with their focus on rote learning and test-taking, work to stymie creativity, leaving children cut off from their natural impulses and boxed in by low expectations. Drawing on cutting-edge research in the realms of biological swarm theory, systems theory, and complexity theory, Rolling shows why group collaboration and adaptive social networking make us both smarter and more creative, and how we can design education and workplace practices around these natural principles, instead of pushing a limited focus on individual achievement that serves neither children nor their future colleagues, managers and mentors. The surprising truth is that the future will be pioneered by the collective problem-solvers, making Swarm Intelligence a must-read for business leaders, educators, and anyone else concerned with nurturing creative intelligence and innovative habits in today's youth.

Swarm Intelligence

The book’s contributing authors are among the top researchers in swarm intelligence. The book is intended to provide an overview of the subject to novices, and to offer researchers an update on interesting recent developments.

Swarm Intelligence

Author: Christian Blum

Publisher: Springer Science & Business Media

ISBN: 3540740899

Page: 286

View: 439

The book’s contributing authors are among the top researchers in swarm intelligence. The book is intended to provide an overview of the subject to novices, and to offer researchers an update on interesting recent developments. Introductory chapters deal with the biological foundations, optimization, swarm robotics, and applications in new-generation telecommunication networks, while the second part contains chapters on more specific topics of swarm intelligence research.

Grokking Artificial Intelligence Algorithms

This. chapter. covers. • Seeing and understanding what inspired swarm
intelligence algorithms • Solving problems with swarm intelligence algorithms •
Designing and implementing an ant colony optimization algorithm ...

Grokking Artificial Intelligence Algorithms

Author: Rishal Hurbans

Publisher: Manning Publications

ISBN: 161729618X

Page: 392

View: 456

Grokking Artificial Intelligence Algorithms is a fully-illustrated and interactive tutorial guide to the different approaches and algorithms that underpin AI. Written in simple language and with lots of visual references and hands-on examples, you’ll learn the concepts, terminology, and theory you need to effectively incorporate AI algorithms into your applications. Summary Grokking Artificial Intelligence Algorithms is a fully-illustrated and interactive tutorial guide to the different approaches and algorithms that underpin AI. Written in simple language and with lots of visual references and hands-on examples, you’ll learn the concepts, terminology, and theory you need to effectively incorporate AI algorithms into your applications. And to make sure you truly grok as you go, you’ll use each algorithm in practice with creative coding exercises—including building a maze puzzle game, performing diamond data analysis, and even exploring drone material optimization. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Artificial intelligence touches every part of our lives. It powers our shopping and TV recommendations; it informs our medical diagnoses. Embracing this new world means mastering the core algorithms at the heart of AI. About the book Grokking Artificial Intelligence Algorithms uses illustrations, exercises, and jargon-free explanations to teach fundamental AI concepts. All you need is the algebra you remember from high school math class. Explore coding challenges like detect­ing bank fraud, creating artistic masterpieces, and setting a self-driving car in motion. What's inside Use cases for different AI algorithms Intelligent search for decision making Biologically inspired algorithms Machine learning and neural networks Reinforcement learning to build a better robot About the reader For software developers with high school–level algebra and calculus skills. About the author Rishal Hurbans is a technologist, startup and AI group founder, and international speaker. Table of Contents 1 Intuition of artificial intelligence 2 Search fundamentals 3 Intelligent search 4 Evolutionary algorithms 5 Advanced evolutionary approaches 6 Swarm intelligence: Ants 7 Swarm intelligence: Particles 8 Machine learning 9 Artificial neural networks 10 Reinforcement learning with Q-learning

Swarm Intelligence

This book constitutes the proceedings of the 11th International Conference on Swarm Intelligence, ANTS 2018, held in Rome, Italy, in October 2018.

Swarm Intelligence

Author: Marco Dorigo

Publisher: Springer

ISBN: 303000533X

Page: 438

View: 574

This book constitutes the proceedings of the 11th International Conference on Swarm Intelligence, ANTS 2018, held in Rome, Italy, in October 2018. The 24 full papers and 12 short papers presented in this volume were carefully reviewed and selected from 69 submissions. They are devoted to the field of swarm intelligence as a whole, without any bias towards specific research directions.

Swarm Intelligence Algorithms

This book presents 24 swarm algorithms together with their modifications and practical applications. Each chapter is devoted to one algorithm.

Swarm Intelligence Algorithms

Author: Adam Slowik

Publisher: CRC Press

ISBN: 0429749473

Page: 349

View: 381

Nature-based algorithms play an important role among artificial intelligence algorithms. Among them are global optimization algorithms called swarm intelligence algorithms. These algorithms that use the behavior of simple agents and various ways of cooperation between them, are used to solve specific problems that are defined by the so-called objective function. Swarm intelligence algorithms are inspired by the social behavior of various animal species, e.g. ant colonies, bird flocks, bee swarms, schools of fish, etc. The family of these algorithms is very large and additionally includes various types of modifications to enable swarm intelligence algorithms to solve problems dealing with areas other than those for which they were originally developed. This book presents 24 swarm algorithms together with their modifications and practical applications. Each chapter is devoted to one algorithm. It contains a short description along with a pseudo-code showing the various stages of its operation. In addition, each chapter contains a description of selected modifications of the algorithm and shows how it can be used to solve a selected practical problem. This book should also be useful for undergraduate and postgraduate students studying nature-based optimization algorithms, and can be a helpful tool for learning these algorithms, along with their modifications and practical applications. In addition, it can be a useful source of knowledge for scientists working in the field of artificial intelligence, as well as for engineers interested in using this type of algorithms in their work. If the reader wishes to expand his knowledge beyond the basics of swarm intelligence algorithms presented in this book and is interested in more detailed information, we recommend the book "Swarm Intelligence Algorithms: A Tutorial" (Edited by A. Slowik, CRC Press, 2020). It contains a detailed explanation of how each algorithm works, along with relevant program codes in Matlab and the C ++ programming language, as well as numerical examples illustrating step-by-step how individual algorithms work.

Swarm Intelligence

The methodological framework of studying swarm intelligence is represented by unconventional computing, robotics, and cognitive science. In this book we aim to analyze new methodologies involved in studying swarm intelligence.

Swarm Intelligence

Author: Andrew Schumann

Publisher: CRC Press

ISBN: 0429647603

Page: 184

View: 371

The notion of swarm intelligence was introduced for describing decentralized and self-organized behaviors of groups of animals. Then this idea was extrapolated to design groups of robots which interact locally to cumulate a collective reaction. Some natural examples of swarms are as follows: ant colonies, bee colonies, fish schooling, bird flocking, horse herding, bacterial colonies, multinucleated giant amoebae Physarum polycephalum, etc. In all these examples, individual agents behave locally with an emergence of their common effect. An intelligent behavior of swarm individuals is explained by the following biological reactions to attractants and repellents. Attractants are biologically active things, such as food pieces or sex pheromones, which attract individuals of swarm. Repellents are biologically active things, such as predators, which repel individuals of swarm. As a consequence, attractants and repellents stimulate the directed movement of swarms towards and away from the stimulus, respectively. It is worth noting that a group of people, such as pedestrians, follow some swarm patterns of flocking or schooling. For instance, humans prefer to avoid a person considered by them as a possible predator and if a substantial part of the group in the situation of escape panic (not less than 5%) changes the direction, then the rest follows the new direction, too. Some swarm patterns are observed among human beings under the conditions of their addictive behavior such as the behavior of alcoholics or gamers. The methodological framework of studying swarm intelligence is represented by unconventional computing, robotics, and cognitive science. In this book we aim to analyze new methodologies involved in studying swarm intelligence. We are going to bring together computer scientists and cognitive scientists dealing with swarm patterns from social bacteria to human beings. This book considers different models of simulating, controlling, and predicting the swarm behavior of different species from social bacteria to humans.

Geospatial Intelligence

That implies either flying a preprogrammed course and returning with stored
video, possibly using artificial intelligence to ... Drones are already flying that use
artificial swarm intelligence relying on communications between the individual ...

Geospatial Intelligence

Author: Robert M. Clark

Publisher: Georgetown University Press

ISBN: 164712011X

Page: 368

View: 529

A riveting introduction to the complex and evolving field of geospatial intelligence. Although geospatial intelligence is a term of recent origin, its underpinnings have a long and interesting history. Geospatial Intelligence: Origins and Evolution shows how the current age of geospatial knowledge evolved from its ancient origins to become ubiquitous in daily life across the globe. Within that framework, the book weaves a tapestry of stories about the people, events, ideas, and technologies that affected the trajectory of what has become known as GEOINT. Author Robert M. Clark explores the historical background and subsequent influence of fields such as geography, cartography, remote sensing, photogrammetry, geopolitics, geophysics, and geographic information systems on GEOINT. Although its modern use began in national security communities, Clark shows how GEOINT has rapidly extended its reach to other government agencies, NGOs, and corporations. This global explosion in the use of geospatial intelligence has far-reaching implications not only for the scientific, academic, and commercial communities but for a society increasingly reliant upon emerging technologies. Drones, the Internet of things, and cellular devices transform how we gather information and how others can collect that information, to our benefit or detriment.

Encyclopedia of Artificial Intelligence

... EA multi-model selection for 520 SVRCACO 410 swarm intelligence (SI) 10,
238, 1309,1531,1536 swarm intelligence approach for adhoc networks 1530
swarm intelligence for visualisation 537 swarm robotics 1537–1542 symbol
grounding ...

Encyclopedia of Artificial Intelligence

Author: Juan Ramon Rabunal

Publisher: IGI Global

ISBN: 1599048507

Page: 1640

View: 587

"This book is a comprehensive and in-depth reference to the most recent developments in the field covering theoretical developments, techniques, technologies, among others"--Provided by publisher.

Knowledge Discovery in Big Data from Astronomy and Earth Observation

15.4.5.3 Swarm Intelligence Swarm intelligence is an innovative intelligent
paradigm which was successfully applied to solve optimization problems. It took
its inspiration from the collective behavior of social swarms in nature such as
flocks of ...

Knowledge Discovery in Big Data from Astronomy and Earth Observation

Author: Petr Skoda

Publisher:

ISBN: 0128191546

Page: 400

View: 412

Knowledge Discovery in Big Data from Astronomy and Earth Observation: Astrogeoinformatics bridges the gap between astronomy and geoscience in the context of applications, techniques and key principles of big data. Machine learning and parallel computing are increasingly becoming cross-disciplinary as the phenomena of Big Data is becoming common place. This book provides insight into the common workflows and data science tools used for big data in astronomy and geoscience. After establishing similarity in data gathering, pre-processing and handling, the data science aspects are illustrated in the context of both fields. Software, hardware and algorithms of big data are addressed. Finally, the book offers insight into the emerging science which combines data and expertise from both fields in studying the effect of cosmos on the earth and its inhabitants.

Swarm Intelligence

This book unleashes a great opportunity for researchers, lecturers, and practitioners interested in Swarm Intelligence, optimization problems, and artificial intelligence.

Swarm Intelligence

Author:

Publisher: BoD – Books on Demand

ISBN: 178984536X

Page: 128

View: 130

Swarm Intelligence has emerged as one of the most studied artificial intelligence branches during the last decade, constituting the fastest growing stream in the bio-inspired computation community. A clear trend can be deduced analyzing some of the most renowned scientific databases available, showing that the interest aroused by this branch has increased at a notable pace in the last years. This book describes the prominent theories and recent developments of Swarm Intelligence methods, and their application in all fields covered by engineering. This book unleashes a great opportunity for researchers, lecturers, and practitioners interested in Swarm Intelligence, optimization problems, and artificial intelligence.

Swarm Intelligence

This book includes 27 chapters and presents a great number of real-world applications of swarm intelligence algorithms and related evolutionary algorithms.

Swarm Intelligence

Author: Ying Tan

Publisher: Control, Robotics and Sensors

ISBN: 1785616315

Page: 880

View: 119

This book includes 27 chapters and presents a great number of real-world applications of swarm intelligence algorithms and related evolutionary algorithms.

Swarm Intelligence

The book provides beginners with a solid foundation of swarm intelligence fundamentals, and offers experts valuable insight into new directions and hybridizations.

Swarm Intelligence

Author: Aboul Hassanien

Publisher:

ISBN:

Page: 210

View: 428

Swarm Intelligence: Principles, Advances, and Applications delivers in-depth coverage of bat, artificial fish swarm, firefly, cuckoo search, flower pollination, artificial bee colony, wolf search, and gray wolf optimization algorithms. The book begins with a brief introduction to mathematical optimization, addressing basic concepts related to swarm intelligence, such as randomness, random walks, and chaos theory. The text then: Describes the various swarm intelligence optimization methods, standardizing the variants, hybridizations, and algorithms whenever possible Discusses variants that focus more on binary, discrete, constrained, adaptive, and chaotic versions of the swarm optimizers Depicts real-world applications of the individual optimizers, emphasizing variable selection and fitness function design Details the similarities, differences, weaknesses, and strengths of each swarm optimization method Draws parallels between the operators and searching manners of the different algorithms Swarm Intelligence: Principles, Advances, and Applications presents a comprehensive treatment of modern swarm intelligence optimization methods, complete with illustrative examples and an extendable MATLAB® package for feature selection in wrapper mode applied on different data sets with benchmarking using different evaluation criteria. The book provides beginners with a solid foundation of swarm intelligence fundamentals, and offers experts valuable insight into new directions and hybridizations.

Innovations in Swarm Intelligence

Among the topics covered in this book include: particle swarm optimization and hybrid methods ant colony optimization and hybrid methods bee colony optimization, glowworm swarm optimization, and complex social swarms application of various ...

Innovations in Swarm Intelligence

Author: Chee Peng Lim

Publisher: Springer Science & Business Media

ISBN: 3642042244

Page: 255

View: 603

Over the past two decades, swarm intelligence has emerged as a powerful approach to solving optimization as well as other complex problems. Swarm intelligence models are inspired by social behaviours of simple agents interacting among themselves as well as with the environment, e.g., flocking of birds, schooling of fish, foraging of bees and ants. The collective behaviours that emerge out of the interactions at the colony level are useful in achieving complex goals. The main aim of this research book is to present a sample of recent innovations and advances in techniques and applications of swarm intelligence. Among the topics covered in this book include: particle swarm optimization and hybrid methods, ant colony optimization and hybrid methods, bee colony optimization, glowworm swarm optimization, and complex social swarms, application of various swarm intelligence models to operational planning of energy plants, modeling and control of nanorobots, classification of documents, identification of disease biomarkers, and prediction of gene signals. The book is directed to researchers, practicing professionals, and undergraduate as well as graduate students of all disciplines who are interested in enhancing their knowledge in techniques and applications of swarm intelligence.

Advances in Swarm Intelligence

The two-volume set of LNCS 10385 and 10386, constitutes the proceedings of the 8th International Conference on Advances in Swarm Intelligence, ICSI 2017, held in Fukuoka, Japan, in July/August 2017.

Advances in Swarm Intelligence

Author: Ying Tan

Publisher: Springer

ISBN: 3319618334

Page: 641

View: 664

The two-volume set of LNCS 10385 and 10386, constitutes the proceedings of the 8th International Conference on Advances in Swarm Intelligence, ICSI 2017, held in Fukuoka, Japan, in July/August 2017. The total of 133 papers presented in these volumes was carefully reviewed and selected from 267 submissions. The paper were organized in topical sections as follows: Part I: theories and models of swarm intelligence; novel swarm-based optimization algorithms; particle swarm optimization; applications of particle swarm optimization; ant colony optimization; artificial bee colony algorithms; genetic algorithms; differential evolution; fireworks algorithm; brain storm optimization algorithm; cuckoo searh; and firefly algorithm. Part II: multi-objective optimization; portfolio optimization; community detection; multi-agent systems and swarm robotics; hybrid optimization algorithms and applications; fuzzy and swarm approach; clustering and forecast; classification and detection; planning and routing problems; dialog system applications; robotic control; and other applications.

Swarm Intelligence

The book provides beginners with a solid foundation of swarm intelligence fundamentals, and offers experts valuable insight into new directions and hybridizations.

Swarm Intelligence

Author: Aboul Ella Hassanien

Publisher: CRC Press

ISBN: 149874107X

Page: 210

View: 818

Swarm Intelligence: Principles, Advances, and Applications delivers in-depth coverage of bat, artificial fish swarm, firefly, cuckoo search, flower pollination, artificial bee colony, wolf search, and gray wolf optimization algorithms. The book begins with a brief introduction to mathematical optimization, addressing basic concepts related to swarm intelligence, such as randomness, random walks, and chaos theory. The text then: Describes the various swarm intelligence optimization methods, standardizing the variants, hybridizations, and algorithms whenever possible Discusses variants that focus more on binary, discrete, constrained, adaptive, and chaotic versions of the swarm optimizers Depicts real-world applications of the individual optimizers, emphasizing variable selection and fitness function design Details the similarities, differences, weaknesses, and strengths of each swarm optimization method Draws parallels between the operators and searching manners of the different algorithms Swarm Intelligence: Principles, Advances, and Applications presents a comprehensive treatment of modern swarm intelligence optimization methods, complete with illustrative examples and an extendable MATLAB® package for feature selection in wrapper mode applied on different data sets with benchmarking using different evaluation criteria. The book provides beginners with a solid foundation of swarm intelligence fundamentals, and offers experts valuable insight into new directions and hybridizations.