Swarm Intelligence and Bio-Inspired Computation: Theory and Applications - Softcover

Softcover

ISBN 10: 1493301365 ISBN 13: 9781493301362

Publisher: Elsevier, 2013

Synopsis

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.

About this title

About the Author

Xin-She Yang obtained his DPhil in Applied Mathematics from the University of Oxford. He then worked at Cambridge University and National Physical Laboratory (UK) as a Senior Research Scientist. He is currently a Reader at Middlesex University London, Adjunct Professor at Reykjavik University (Iceland) and Guest Professor at Xi’an Polytechnic University (China). He is an elected Bye-Fellow at Downing College, Cambridge University. He is also the IEEE CIS Chair for the Task Force on Business Intelligence and Knowledge Management, and the Editor-in-Chief of International Journal of Mathematical Modelling and Numerical Optimisation (IJMMNO).

Edition details

• Publisher: Elsevier
• Publication date: 2013
• Language: English
• ISBN 10: 1493301365
• ISBN 13: 9781493301362
• Binding: Paperback
• Edition number: 1
• Number of pages: 450
• Editor: Yang Xin-She, Cui Zhihua, Xiao Renbin, Gandomi Amir Hossein, Karamanoglu Mehmet