Nature Inspired Computing For Control Systems

Nature-Inspired Computing for Control Systems

The book presents recent advances in nature-inspired computing, giving a special emphasis to control systems applications. It reviews different techniques used for simulating physical, chemical, biological or social phenomena at the purpose of designing robust, predictive and adaptive control strategies. The book is a collection of several contributions, covering either more general approaches in control systems, or methodologies for control tuning and adaptive controllers, as well as exciting applications of nature-inspired techniques in robotics. On one side, the book is expected to motivate readers with a background in conventional control systems to try out these powerful techniques inspired by nature. On the other side, the book provides advanced readers with a deeper understanding of the field and a broad spectrum of different methods and techniques. All in all, the book is an outstanding, practice-oriented reference guide to nature-inspired computing addressing graduate students, researchers and practitioners in the field of control engineering.

Nature-Inspired Computing and Optimization

The book provides readers with a snapshot of the state of the art in the field of nature-inspired computing and its application in optimization. The approach is mainly practice-oriented: each bio-inspired technique or algorithm is introduced together with one of its possible applications. Applications cover a wide range of real-world optimization problems: from feature selection and image enhancement to scheduling and dynamic resource management, from wireless sensor networks and wiring network diagnosis to sports training planning and gene expression, from topology control and morphological filters to nutritional meal design and antenna array design. There are a few theoretical chapters comparing different existing techniques, exploring the advantages of nature-inspired computing over other methods, and investigating the mixing time of genetic algorithms. The book also introduces a wide range of algorithms, including the ant colony optimization, the bat algorithm, genetic algorithms, the collision-based optimization algorithm, the flower pollination algorithm, multi-agent systems and particle swarm optimization. This timely book is intended as a practice-oriented reference guide for students, researchers and professionals.

Handbook of Research on Soft Computing and Nature-inspired Algorithms