Swarm Intelligence Principles, Advances, And Applications

This book constitutes the thoroughly refereed post-conference proceedings of the Second International Conference on Swarm Intelligence Based Optimization, ICSIBO 2016, held in Mulhouse, France, in June 2016.

This book offers a comprehensive overview of the latest developments in unmanned aerial systems (UAS) technology and their potential applications. It covers a broad spectrum of topics related to UAS, such as Autonomy and Control, Flight Dynamic and Control, Communication and Navigation, Sensing and Perception, Design and Simulation, and Materials and Mechanics. The book is targeted toward a technical audience, including researchers, engineers, and practitioners working in the field of unmanned aerial systems. It provides an in-depth and detailed treatment of the subject matter, offering thorough discussions of the underlying principles and technical details of UAS technology.

This book presents a number of research efforts in combining AI methods or techniques to solve complex problems in various areas. The combination of different intelligent methods is an active research area in artificial intelligence (AI), since it is believed that complex problems can be more easily solved with integrated or hybrid methods, such as combinations of different soft computing methods (fuzzy logic, neural networks, and evolutionary algorithms) among themselves or with hard AI technologies like logic and rules; machine learning with soft computing and classical AI methods; and agent-based approaches with logic and non-symbolic approaches.

This book presents a number of research efforts in combining AI methods or techniques to solve complex problems in various areas. The combination of different intelligent methods is an active research area in artificial intelligence (AI), since it is believed that complex problems can be more easily solved with integrated or hybrid methods, such as combinations of different soft computing methods (fuzzy logic, neural networks, and evolutionary algorithms) among themselves or with hard AI technologies like logic and rules; machine learning with soft computing and classical AI methods; and agent-based approaches with logic and non-symbolic approaches.