Recent Trends in Swarm Intelligence Enabled Research for Engineering Applications
Series: Hybrid Computational Intelligence for Pattern Analysis and Understanding;
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Product details:
- Publisher Elsevier Science
- Date of Publication 16 July 2024
- ISBN 9780443155338
- Binding Paperback
- No. of pages380 pages
- Size 229x152 mm
- Weight 450 g
- Language English 579
Categories
Long description:
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
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