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This book is devoted to the state-of-the-art in all aspects of fireworks algorithm (FWA), with particular emphasis on the efficient improved versions of FWA. It describes the most substantial theoretical analysis including basic principle and implementation of FWA and modeling and theoretical analysis of FWA. It covers exhaustively the key recent significant research into the improvements of FWA so far. In addition, the book describes a few advanced topics in the research of FWA, including multi-objective optimization (MOO), discrete FWA (DFWA) for combinatorial optimization, and GPU-based FWA for parallel implementation. In sequels, several successful applications of FWA on non-negative matrix factorization (NMF), text clustering, pattern recognition, and seismic inversion problem, and swarm robotics, are illustrated in details, which might shed new light on more real-world applications in future. Addressing a multidisciplinary topic, it will appeal to researchers and professionals in the areas of metahuristics, swarm intelligence, evolutionary computation, complex optimization solving, etc.
In recent years, swarm intelligence has become a popular computational approach among researchers working on optimization problems throughout the globe. Several algorithms inside swarm intelligence have been implemented due to their application to real-world issues and other advantages. A specific procedure, Fireworks Algorithm, is an emerging method that studies the explosion process of fireworks within local areas. Applications of this developing program are undiscovered, and research is necessary for scientists to fully understand the workings of this innovative system. The Handbook of Research on Fireworks Algorithms and Swarm Intelligence is a pivotal reference source that provides vital research on theory analysis, improvements, and applications of fireworks algorithm. While highlighting topics such as convergence rate, parameter applications, and global optimization analysis, this publication explores up-to-date progress on the specific techniques of this algorithm. This book is ideally designed for researchers, data scientists, mathematicians, engineers, software developers, postgraduates, and academicians seeking coverage on this evolutionary computation method.
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.
""This book provides vital research on theory analysis, improvements, and applications of fireworks algorithm. While highlighting topics such as convergence rate, parameter applications, and global optimization analysis, this publication explores up-to-date progress on the specific techniques of this algorithm"--Provided by publisher"--
Studies on robotics applications have grown substantially in recent years, with swarm robotics being a relatively new area of research. Inspired by studies in swarm intelligence and robotics, swarm robotics facilitates interactions between robots as well as their interactions with the environment. The Handbook of Research on Design, Control, and Modeling of Swarm Robotics is a collection of the most important research achievements in swarm robotics thus far, covering the growing areas of design, control, and modeling of swarm robotics. This handbook serves as an essential resource for researchers, engineers, graduates, and senior undergraduates with interests in swarm robotics and its applications.
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.
This book constitutes the proceedings of the 11th International Conference on Advances in Swarm Intelligence, ICSI 2020, held in July 2020 in Belgrade, Serbia. Due to the COVID-19 pandemic the conference was held virtually. The 63 papers included in this volume were carefully reviewed and selected from 127 submissions. The papers are organized in 12 cohesive topical sections as follows: Swarm intelligence and nature-inspired computing; swarm-based computing algorithms for optimization; particle swarm optimization; ant colony optimization; brain storm optimization algorithm; bacterial foraging optimization; genetic algorithm and evolutionary computation; multi-objective optimization; machine learning; data mining; multi-agent system and robotic swarm, and other applications.
As today’s world continues to advance, Artificial Intelligence (AI) is a field that has become a staple of technological development and led to the advancement of numerous professional industries. An application within AI that has gained attention is machine learning. Machine learning uses statistical techniques and algorithms to give computer systems the ability to understand and its popularity has circulated through many trades. Understanding this technology and its countless implementations is pivotal for scientists and researchers across the world. The Handbook of Research on Emerging Trends and Applications of Machine Learning provides a high-level understanding of various machine learning algorithms along with modern tools and techniques using Artificial Intelligence. In addition, this book explores the critical role that machine learning plays in a variety of professional fields including healthcare, business, and computer science. While highlighting topics including image processing, predictive analytics, and smart grid management, this book is ideally designed for developers, data scientists, business analysts, information architects, finance agents, healthcare professionals, researchers, retail traders, professors, and graduate students seeking current research on the benefits, implementations, and trends of machine learning.
This book introduces readers to genetic algorithms (GAs) with an emphasis on making the concepts, algorithms, and applications discussed as easy to understand as possible. Further, it avoids a great deal of formalisms and thus opens the subject to a broader audience in comparison to manuscripts overloaded by notations and equations. The book is divided into three parts, the first of which provides an introduction to GAs, starting with basic concepts like evolutionary operators and continuing with an overview of strategies for tuning and controlling parameters. In turn, the second part focuses on solution space variants like multimodal, constrained, and multi-objective solution spaces. Lastly, the third part briefly introduces theoretical tools for GAs, the intersections and hybridizations with machine learning, and highlights selected promising applications.
Soft computing and nature-inspired computing both play a significant role in developing a better understanding to machine learning. When studied together, they can offer new perspectives on the learning process of machines. The Handbook of Research on Soft Computing and Nature-Inspired Algorithms is an essential source for the latest scholarly research on applications of nature-inspired computing and soft computational systems. Featuring comprehensive coverage on a range of topics and perspectives such as swarm intelligence, speech recognition, and electromagnetic problem solving, this publication is ideally designed for students, researchers, scholars, professionals, and practitioners seeking current research on the advanced workings of intelligence in computing systems.