About the Book
Recent Trends in Swarm Intelligence Enabled Research for Engineering Applications focuses on recent, up-to-date technologies, combining other intelligent tools with swarm intelligence techniques to yield robust and failsafe solutions to real world problems. This book aims to provide audiences with a platform to learn and gain insights into the latest developments in hybrid swarm intelligence. It will be useful to researchers, engineers, developers, practitioners, and graduate students working in the major and interdisciplinary areas of computational intelligence, communication systems, computer networks, and soft computing. With the advent of data-intensive applications, the elimination of redundancy in disseminated information has become a serious challenge for researchers who are on the lookout for evolving metaheuristic algorithms which can explore and exploit the information feature space to derive the optimal settings for specific applications. Swarm intelligence algorithms have developed as one of the most widely used metaheuristic techniques for addressing this challenge in an effective way. Inspired by the behavior of a swarm of bees, these swarm intelligence techniques emulate the corresponding natural instincts to derive optimal solutions for data-intensive applications.
Table of Contents:
Part I: Swarm Intelligence 1. Fundamentals of Swarm Intelligence 2. Group foraging of social insects 3. Division of labor 4. Nest-building of social insects 5. Collective sorting and clustering 6. Multi-objective optimization 7. Swarm-based web intelligence 8. Swarm intelligent control systems Part II: Applications 9. Signal Processing 10. Big Data Analytics 11. Communication, Networking & Information Engineering 12. Bioinformatics & Biomedical Engineering 13. Innovative Intelligent Systems & Applications 14. Swarm Intelligent Controllers 15. Optimization in Federated Learning Systems 16. Optimization of Cloud, Fog and Edge Computing Systems 17. Blockchain and IoT Part III: Hybrid Swarm Intelligence Techniques 18. Adaptive swarm intelligent systems 19. Quantum-inspired swarm intelligence 20. Neuro-Fuzzy Swarm Intelligence 21. Rough-Neuro Swarm Intelligence 22. Conclusion – Editors
About the Author :
Siddhartha Bhattacharyya is a Senior Researcher in the Faculty of Electrical Engineering and Computer Science of VSB Technical University of Ostrava, Czech Republic. He is also serving as the Scientific Advisor of Algebra University College, Zagreb, Croatia. Prior to this, he served as the Principal of Rajnagar Mahavidyalaya, Rajnagar, Birbhum. He was a professor at CHRIST (Deemed to be University), Bangalore, India, and also served as the Principal of RCC Institute of Information Technology, Kolkata, India. He is the recipient of several coveted national and international awards. He received the Honorary Doctorate Award (D. Litt.) from the University of South America and the SEARCC International Digital Award ICT Educator of the Year in 2017. He was appointed as the ACM Distinguished Speaker for the tenure 2018-2020. He has been appointed as the IEEE Computer Society Distinguished Visitor for the tenure 2021-2023. He has co-authored six books, co-edited 75 books, and has more than 300 research publications in international journals and conference proceedings to his credit. Mario Köppen is a professor at the Network Design and Reserach Center (NDRC) of the Kyushu Institute of Technology, where he is conducting research in the fields of multi-objective optimization, digital convergence, and multimodal content management. He studied physics at the Humboldt-University of Berlin and received his master’s degree in solid state physics in 1991. He has published around 100 peer-reviewed papers in conference proceedings, journals and books and was active in the organization of various conferences as chair or member of the program committee, including the WSC on-line conference series on Soft Computing in Industrial Applications, and the HIS conference series on Hybrid Intelligent Systems. He is founding member of the World Federation of Soft Computing, editorial board member of the Applied Soft Computing journal, the International Journal on Hybrid Intelligent Systems and the International Journal on Computational Intelligence Research.
Debashis De is a professor at MAKAUT, WB, India. He is a senior member-IEEE, fellow IETE, and life member CSI. He was awarded the prestigious Boyscast Fellowship by the Department of Science and Technology, India, to work at the Heriot-Watt University, Scotland, UK. He received the Endeavour Fellowship Award from DEST Australia to work at the University of Western Australia and received the Young Scientist twice in New Delhi and in Istanbul, Turkey, from the International Union of Radio Science, Belgium. He established the Center of Mobile cloud computing (CMCC) for IoT applications, and is vice-chair of Dew Computing STC of IEEE Computer Society. Dr. De has published in 320 journals and 200 conference papers, 15 books, and filed ten patents. His research interests are in edge AI, IoT, and quantum computing. Dr. B K Panigrahi is a Professor in the Electrical Engineering Department, IIT Delhi, India. Prior to joining IIT Delhi in 2005, he has served as a faculty in Electrical Engineering Department, UCE Burla, Odisha, India from 1992 to 2005. Dr Panigrahi is a senior member of IEEE and Fellow of INAE, India. His research interest includes application of soft computing and evolutionary computing techniques to power system planning, operation and control. He has also working in the field of bio-medical signal processing and image processing. He has served as the editorial board member, associate editor, and special issue guest editor of different international journals. He is also associated with various international conferences in various capacities. Dr. Panigrahi has published more than 100 research papers in various international and national journals.
