Recent Trends in Swarm Intelligence Enabled Research for Engineering Applications: Hybrid Computational Intelligence for Pattern Analysis and Understanding
Editat de Siddhartha Bhattacharyya, Mario Köppen, Debashis De, Bijaya Ketan Panigrahien Limba Engleză Paperback – 16 iul 2024
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.
- 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
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Specificații
ISBN-13: 9780443155338
ISBN-10: 044315533X
Pagini: 380
Dimensiuni: 152 x 229 x 23 mm
Greutate: 0.6 kg
Editura: ELSEVIER SCIENCE
Seria Hybrid Computational Intelligence for Pattern Analysis and Understanding
ISBN-10: 044315533X
Pagini: 380
Dimensiuni: 152 x 229 x 23 mm
Greutate: 0.6 kg
Editura: ELSEVIER SCIENCE
Seria Hybrid Computational Intelligence for Pattern Analysis and Understanding
Cuprins
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
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