Aboul Ella Hassanien

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 possibleDiscusses variants that focus more on binary, discrete, constrained, adaptive, and chaotic versions of the swarm optimizersDepicts real-world applications of the individual optimizers, emphasizing variable selection and fitness function designDetails the similarities, differences, weaknesses, and strengths of each swarm optimization methodDraws parallels between the operators and searching manners of the different algorithmsSwarm 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.

This book provides a collection of high-quality research works that address broad challenges in both theoretical and applied aspects of dynamic wireless sensor networks (WSNs) for intelligent and smart applications in a variety of environments. It presents the most central concepts associated with Dynamic Wireless Sensor Networks applications, and discusses issues surrounding Wireless Sensor Network Structures for complex and mobile-based applications. The book subsequently discusses several topics related to energy management in dynamic WSNs, and reviews the steps involved in building a secure and trusted data transmission model using the WSN applications of intelligent environments. Lastly, it discusses the applications of WSNs in live data systems such as SCADA systems. Readers will discover a collection of high-quality research works that address broad challenges in both theoretical and applied aspects of WSNs for intelligent real-life applications. In addition, the book presents original research on the application of a dynamic WSN to solve the problem of energy consumption in a secure WSN during the process of data aggregation and transmission. Written by respected experts in the field, the book will stimulate further efforts in the application of the intelligent WSNs model, helping to solve the problem of data processing in a limited resource WSN-based environment.

This book provides a collection of high-quality research works that address broad challenges in both theoretical and applied aspects of dynamic wireless sensor networks (WSNs) for intelligent and smart applications in a variety of environments. It presents the most central concepts associated with Dynamic Wireless Sensor Networks applications, and discusses issues surrounding Wireless Sensor Network Structures for complex and mobile-based applications. The book subsequently discusses several topics related to energy management in dynamic WSNs, and reviews the steps involved in building a secure and trusted data transmission model using the WSN applications of intelligent environments. Lastly, it discusses the applications of WSNs in live data systems such as SCADA systems. Readers will discover a collection of high-quality research works that address broad challenges in both theoretical and applied aspects of WSNs for intelligent real-life applications. In addition, the book presents original research on the application of a dynamic WSN to solve the problem of energy consumption in a secure WSN during the process of data aggregation and transmission. Written by respected experts in the field, the book will stimulate further efforts in the application of the intelligent WSNs model, helping to solve the problem of data processing in a limited resource WSN-based environment.

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 possibleDiscusses variants that focus more on binary, discrete, constrained, adaptive, and chaotic versions of the swarm optimizersDepicts real-world applications of the individual optimizers, emphasizing variable selection and fitness function designDetails the similarities, differences, weaknesses, and strengths of each swarm optimization methodDraws parallels between the operators and searching manners of the different algorithmsSwarm 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.

Explores the application of rough set theory, which has attracted attention because of the ability to enhance databases by allowing for the management of uncertainty, a comparative analysis between rough sets, and other intelligent data analysis.