Emerging Research On Swarm Intelligence And Algorithm Optimization
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Emerging Research on Swarm Intelligence and Algorithm Optimization by Shi, Yuhui Pdf
Throughout time, scientists have looked to nature in order to understand and model solutions for complex real-world problems. In particular, the study of self-organizing entities, such as social insect populations, presents a new opportunity within the field of artificial intelligence. Emerging Research on Swarm Intelligence and Algorithm Optimization discusses current research analyzing how the collective behavior of decentralized systems in the natural world can be applied to intelligent system design. Discussing the application of swarm principles, optimization techniques, and key algorithms being used in the field, this publication serves as an essential reference for academicians, upper-level students, IT developers, and IT theorists.
Handbook of Research on Swarm Intelligence in Engineering by Bhattacharyya, Siddhartha Pdf
Swarm Intelligence has recently emerged as a next-generation methodology belonging to the class of evolutionary computing. As a result, scientists have been able to explain and understand real-life processes and practices that previously remained unexplored. The Handbook of Research on Swarm Intelligence in Engineering presents the latest research being conducted on diverse topics in intelligence technologies such as Swarm Intelligence, Machine Intelligence, Optical Engineering, and Signal Processing with the goal of advancing knowledge and applications in this rapidly evolving field. The enriched interdisciplinary contents of this book will be a subject of interest to the widest forum of faculties, existing research communities, and new research aspirants from a multitude of disciplines and trades.
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 by Abhishek Sharma,Abhinav Sharma,Jitendra Kumar Pandey,Mangey Ram Pdf
Swarm intelligence is one of the fastest growing subfields of artificial intelligence and soft computing. This field includes multiple optimization algorithms to solve NP-hard problems for which conventional methods are not effective. It inspires researchers in engineering sciences to learn theories from nature and incorporate them. Swarm Intelligence: Foundation, Principles, and Engineering Applications provides a comprehensive review of new swarm intelligence techniques and offers practical implementation of Particle Swarm Optimization (PSO) with MATLAB code. The book discusses the statistical analysis of swarm optimization techniques so that researchers can analyse their experiment design. It also includes algorithms in social sectors, oil and gas industries, and recent research findings of new optimization algorithms in the field of engineering describing the implementation in machine learning. This book is written for students of engineering, research scientists, and academicians involved in the engineering sciences.
Swarm Intelligence by Christian Blum,Daniel Merkle Pdf
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.
Swarm Intelligence Optimization by Abhishek Kumar,Pramod Singh Rathore,Vicente Garcia Diaz,Rashmi Agrawal Pdf
Resource optimization has always been a thrust area of research, and as the Internet of Things (IoT) is the most talked about topic of the current era of technology, it has become the need of the hour. Therefore, the idea behind this book was to simplify the journey of those who aspire to understand resource optimization in the IoT. To this end, included in this book are various real-time/offline applications and algorithms/case studies in the fields of engineering, computer science, information security, and cloud computing, along with the modern tools and various technologies used in systems, leaving the reader with a high level of understanding of various techniques and algorithms used in resource optimization.
Handbook of Research on Advancements of Swarm Intelligence Algorithms for Solving Real-World Problems by Cheng, Shi,Shi, Yuhui Pdf
The use of optimization algorithms has seen an emergence in various professional fields due to its ability to process data and information in an efficient and productive manner. Combining computational intelligence with these algorithms has created a trending subject of research on how much more beneficial intelligent-inspired algorithms can be within companies and organizations. As modern theories and applications are continually being developed in this area, professionals are in need of current research on how intelligent algorithms are advancing in the real world. TheHandbook of Research on Advancements of Swarm Intelligence Algorithms for Solving Real-World Problems is a pivotal reference source that provides vital research on the development of swarm intelligence algorithms and their implementation into current issues. While highlighting topics such as multi-agent systems, bio-inspired computing, and evolutionary programming, this publication explores various concepts and theories of swarm intelligence and outlines future directions of development. This book is ideally designed for IT specialists, researchers, academicians, engineers, developers, practitioners, and students seeking current research on the real-world applications of intelligent algorithms.
Innovations in Swarm Intelligence by Chee Peng Lim,Satchidananda Dehuri Pdf
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.
What Is Swarm Intelligence - Traders deciding on the next big market bet. - A navigation app quickly mapping out a less-explored area. - Fashion brands choosing the hottest color of the season. - An airport managing fight delays. What do these scenarios have in common? In each one, swarm intelligence blends global and local insight to improve how businesses make decisions. Swarm intelligence is a form of artificial intelligence (AI) inspired by the insect kingdom. In nature, it describes how honeybees migrate, how ants form perfect trails, and how birds flock. In the world of AI, swarm systems draw input from individual people or machine sensors and then use algorithms to optimize the overall performance of the group or system in real time. Consider Waze, the popular road navigation app that uses swarm intelligence to create and modify maps. Starting with limited digital maps, it began making tweaks based on its users’ GPS data along with manual map modifications by registered users. Entire cities have been mapped using this method, as was the case in Costa Rica’s capital, San José. And just as ants signal danger to their counterparts, so too do Waze users contribute live information from accident locations and traffic jams. Swarm intelligence is now being used to predict everything from the outcome of the Super Bowl to fashion trends to major political events. Using swarm intelligence, investors can better predict market movements, and retailers can more accurately forecast sales. How You Will Benefit By the end of reading this book, you will have the answers to the public top 100 questions, queries, issues, doubts, problems and inquiries. Most importantly, you will be able master the discussion about the following topics in Swarm Intelligence, and explore the new ways of thinking about life and business: 01 - Fundamental Concepts: Definition, Systems, Nature 02 - Models of Swarm Behavior: Boids, Self-Propelled Particles 03 - Optimization Problem: Elements, Formulations, and Search Solutions 04 - Meta-Heuristic Nature Inspired Optimization Algorithms Inspired by Swarm Intelligence 05 - Meta-Heuristic and Monkeys Problems: Infinite, Finite, and the difference 06 - Common Algorithmic Characteristics and Comparisons: Ant Colony Optimization, Bee Colony Optimization, Bat Algorithm, Cuckoo Search, Particle Swarm Optimization, Firefly Algorithm, Flower Pollination Algorithm, Swarm Intelligence Application Areas, Travelling Salesman Problem, Telecommunication, Image Processing, Engineering Design, Vehicle Routing 07 - Swarm Intelligence Systems: Taxonomy, Natural vs. Artificial, Scientific vs. Engineering 08 - Examples of Swarm Intelligence Systems: Foraging Behavior of Ants, Clustering by a Swarm of Robots, Exploitation of Collective Behaviors of Animal Societies, Swarm-based Data Analysis 09 - Properties of Swarm Intelligence Systems: Individual, Homogeneous, Interaction, Self-Organized 10 - Studies and Applications of Swarm Intelligence Systems: Clustering Behavior of Ants, Nest Building Behavior of Wasp and Termites, Flocking and Schooling in Birds and Fish, Any Colony Optimization, Particle Swarm Optimization, Swarm-based Network Management, Cooperative Behavior in Swarm of Robots. 11- Swarm Intelligence as a Whole New Way of Thinking About Business: Perspective and Advantages 12 - Swarm Intelligence Foraging for Solutions in Telecommunication, Information Technology, Logistics, Manufacturing. 13 - Advantages of Swarm Intelligence for Organizations: Simple Rules Rule, Raiding New Markets, A swarm of Possibilities.
New Advancements in Swarm Algorithms: Operators and Applications by Erik Cuevas,Fernando Fausto,Adrián González Pdf
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.
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 and Bio-Inspired Computation by Xin-She Yang,Zhihua Cui,Renbin Xiao,Amir Hossein Gandomi,Mehmet Karamanoglu Pdf
Swarm Intelligence and bio-inspired computation have become increasing popular in the last two decades. Bio-inspired algorithms such as ant colony algorithms, bat algorithms, bee algorithms, firefly algorithms, cuckoo search and particle swarm optimization have been applied in almost every area of science and engineering with a dramatic increase of number of relevant publications. This book reviews the latest developments in swarm intelligence and bio-inspired computation from both the theory and application side, providing a complete resource that analyzes and discusses the latest and future trends in research directions. It can help new researchers to carry out timely research and inspire readers to develop new algorithms. With its impressive breadth and depth, this book will be useful for advanced undergraduate students, PhD students and lecturers in computer science, engineering and science as well as researchers and engineers. Focuses on the introduction and analysis of key algorithms Includes case studies for real-world applications Contains a balance of theory and applications, so readers who are interested in either algorithm or applications will all benefit from this timely book.
GPU-based Parallel Implementation of Swarm Intelligence Algorithms by Ying Tan Pdf
GPU-based Parallel Implementation of Swarm Intelligence Algorithms combines and covers two emerging areas attracting increased attention and applications: graphics processing units (GPUs) for general-purpose computing (GPGPU) and swarm intelligence. This book not only presents GPGPU in adequate detail, but also includes guidance on the appropriate implementation of swarm intelligence algorithms on the GPU platform. GPU-based implementations of several typical swarm intelligence algorithms such as PSO, FWA, GA, DE, and ACO are presented and having described the implementation details including parallel models, implementation considerations as well as performance metrics are discussed. Finally, several typical applications of GPU-based swarm intelligence algorithms are presented. This valuable reference book provides a unique perspective not possible by studying either GPGPU or swarm intelligence alone. This book gives a complete and whole picture for interested readers and new comers who will find many implementation algorithms in the book suitable for immediate use in their projects. Additionally, some algorithms can also be used as a starting point for further research. Presents a concise but sufficient introduction to general-purpose GPU computing which can help the layman become familiar with this emerging computing technique Describes implementation details, such as parallel models and performance metrics, so readers can easily utilize the techniques to accelerate their algorithmic programs Appeals to readers from the domain of high performance computing (HPC) who will find the relatively young research domain of swarm intelligence very interesting Includes many real-world applications, which can be of great help in deciding whether or not swarm intelligence algorithms or GPGPU is appropriate for the task at hand
What Is Swarm Intelligence The collective behavior of decentralized, self-organized systems, either natural or artificial, is what is referred to as swarm intelligence (SI). The idea is used in research that is being done on artificial intelligence. In 1989, Gerardo Beni and Jing Wang were the ones who first used the phrase "cellular robotic systems" in connection with their respective fields of study. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Swarm intelligence Chapter 2: Genetic algorithm Chapter 3: Simulated annealing Chapter 4: Evolutionary algorithm Chapter 5: Swarm behaviour Chapter 6: Evolutionary computation Chapter 7: Particle swarm optimization Chapter 8: Boids Chapter 9: Ant colony optimization algorithms Chapter 10: Metaheuristic Chapter 11: Marco Dorigo Chapter 12: Stochastic diffusion search Chapter 13: Cultural algorithm Chapter 14: Parallel metaheuristic Chapter 15: Hyper-heuristic Chapter 16: Ant robotics Chapter 17: Cuckoo search Chapter 18: Meta-optimization Chapter 19: Glossary of artificial intelligence Chapter 20: List of metaphor-based metaheuristics Chapter 21: Table of metaheuristics (II) Answering the public top questions about swarm intelligence. (III) Real world examples for the usage of swarm intelligence in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of swarm intelligence' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of swarm intelligence.
Critical Developments and Applications of Swarm Intelligence by Shi, Yuhui Pdf
Artificial intelligence is a constantly advancing field that requires models in order to accurately create functional systems. The use of natural acumen to create artificial intelligence creates a field of research in which the natural and the artificial meet in a new and innovative way. Critical Developments and Applications of Swarm Intelligence is a critical academic publication that examines developing research, technologies, and function regarding natural and artificial acumen specifically, in regards to self-organized systems. Featuring coverage on a broad range of topics such as evolutionary algorithms, optimization techniques, and computational comparison, this book is geared toward academicians, students, researchers, and engineers seeking relevant and current research on the progressive research based on the implementation of swarm intelligence in self-organized systems.