Recent Trends in Swarm Intelligence Enabled Research for Engineering Applications Hybrid Computational Intelligence for Pattern Analysis and Understanding Series

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.

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

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.

• Introduces the theory underpinning hybrid swarm intelligence-enabled research as well as the leading applications across the fields of communication, networking, and information engineering
• Presents a range of applications research, including signal processing, communication engineering, bioinformatics, controllers, federated learning systems, blockchain, and IoT
• Includes case studies and code snippets in applications chapters