Editors:
- Includes prominent theories and recent developments of swarm intelligence methods
- Presents nature-inspired methods for efficient optimization and problem solving
- Serves as a reference resource for researchers and practitioners in academia and industry
Part of the book series: Springer Tracts in Nature-Inspired Computing (STNIC)
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Table of contents (10 chapters)
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Front Matter
About this book
Editors and Affiliations
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Tecnalia Research and Innovation, BRTA (Basque Research and Technology Alliance), Derio, Spain
Eneko Osaba
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Department of Design Engineering and Mathematics, School of Science and Technology, Middlesex University, London, UK
Xin-She Yang
About the editors
Xin-She Yang obtained his D.Phil. in applied mathematics from the University of Oxford. He then worked at Cambridge University and National Physical Laboratory (UK) as Senior research Scientist. Now he is a reader/professor at Middlesex University London, and the IEEE CIS chair for the task force on business intelligence and knowledge management. With more than 20 years' teaching and research experience, he has authored 15 books and edited 25 books. He has published more than 250 peer-reviewed research papers with nearly 55,000 citations. According to Clarivate Analytics/Web of Sciences, he has been on the prestigious list of highly cited researchers for five consecutive years (2016–2020).
Bibliographic Information
Book Title: Applied Optimization and Swarm Intelligence
Editors: Eneko Osaba, Xin-She Yang
Series Title: Springer Tracts in Nature-Inspired Computing
DOI: https://doi.org/10.1007/978-981-16-0662-5
Publisher: Springer Singapore
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021
Hardcover ISBN: 978-981-16-0661-8Published: 18 May 2021
Softcover ISBN: 978-981-16-0664-9Published: 19 May 2022
eBook ISBN: 978-981-16-0662-5Published: 17 May 2021
Series ISSN: 2524-552X
Series E-ISSN: 2524-5538
Edition Number: 1
Number of Pages: XI, 229
Number of Illustrations: 21 b/w illustrations, 26 illustrations in colour
Topics: Computational Intelligence, Artificial Intelligence, Algorithms, Optimization