Improved Bacterial Foraging Optimization Algorithm with Comprehensive Swarm Learning Strategies
Bacterial foraging optimization (BFO), a novel bio-inspired heuristic optimization algorithm, has been attracted widespread attention and widely applied to various practical optimization problems. However, the standard BFO algorithm exists some potential
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Ying Tan Yuhui Shi Milan Tuba (Eds.)
Advances in Swarm Intelligence 11th International Conference, ICSI 2020 Belgrade, Serbia, July 14–20, 2020 Proceedings
Lecture Notes in Computer Science Founding Editors Gerhard Goos Karlsruhe Institute of Technology, Karlsruhe, Germany Juris Hartmanis Cornell University, Ithaca, NY, USA
Editorial Board Members Elisa Bertino Purdue University, West Lafayette, IN, USA Wen Gao Peking University, Beijing, China Bernhard Steffen TU Dortmund University, Dortmund, Germany Gerhard Woeginger RWTH Aachen, Aachen, Germany Moti Yung Columbia University, New York, NY, USA
12145
More information about this series at http://www.springer.com/series/7407
Ying Tan Yuhui Shi Milan Tuba (Eds.) •
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Advances in Swarm Intelligence 11th International Conference, ICSI 2020 Belgrade, Serbia, July 14–20, 2020 Proceedings
123
Editors Ying Tan Peking University Beijing, China Milan Tuba Singidunum University Belgrade, Serbia
Yuhui Shi Southern University of Science and Technology Shenzhen, China
ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Computer Science ISBN 978-3-030-53955-9 ISBN 978-3-030-53956-6 (eBook) https://doi.org/10.1007/978-3-030-53956-6 LNCS Sublibrary: SL1 – Theoretical Computer Science and General Issues © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface
This book, LNCS vol. 12145, constitutes the proceedings of the 11th International Conference on Swarm Intelligence (ICSI 2020) held virtually online during July 14–20, 2020, due to the pandemic of COVID-19. The theme of ICSI 2020 was “Serving Life with Swarm Intelligence.” ICSI 2020 provided an excellent opportunity and/or an academic forum for a
