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.
Recent Trends in Swarm Intelligence Enabled Research for Engineering Applications focuses on the recent and most 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.
1. Recent Trends in Swarm Intelligence Enabled Research for Engineering Applications - p. i 2. Cover - p. i 3. Title Page - p. iii 4. Copyright - p. iv 5. Dedication - p. v 6. Table of Contents - p. v 7. Contributors - p. xi 8. Preface - p. xiii 9. Chapter 1 Emerging trends in computational swarm intelligence: A comprehensive overview - p. 1 10. Chapter 2 Innovative intelligent systems and applications: A Swarm intelligence perspective - p. 41 11. Chapter 3 Medical image analysis using swarm intelligence: A survey - p. 89 12. Chapter 4 An optimal and robust segmentation framework for analysis and detection of brain tumor in MRI images - p. 131 13. Chapter 5 Autoimmune diseases and an approach to type 1 diabetes analysis using PSO, K-means, and silhouette values - p. 159 14. Chapter 6 Consumer portfolio management with usage of evolutionary approach by PSO algorithm and machine learning models - p. 187 15. Chapter 7 Portfolio optimization using simulated annealing and quantum-inspired simulated annealing: A comparative study - p. 213 16. Chapter 8 Design of vehicular ad hoc network using IoT based on particle swarm optimization technique - p. 245 17. Chapter 9 Modeling and parametric optimization of grinding process using flower pollination algorithm - p. 265 18. Chapter 10 New quantum-inspired salp swarm algorithm: A comparative study on numerical computation - p. 291 19. Chapter 11 A fuzzy soft coronavirus alarm model - p. 331 20. Chapter 12 Conclusion and future research directions - p. 343 21. Index - p. 349
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.
Recent Trends in Swarm Intelligence Enabled Research for Engineering Applications focuses on the recent and most 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.
1. Recent Trends in Swarm Intelligence Enabled Research for Engineering Applications - p. i 2. Cover - p. i 3. Title Page - p. iii 4. Copyright - p. iv 5. Dedication - p. v 6. Table of Contents - p. v 7. Contributors - p. xi 8. Preface - p. xiii 9. Chapter 1 Emerging trends in computational swarm intelligence: A comprehensive overview - p. 1 10. Chapter 2 Innovative intelligent systems and applications: A Swarm intelligence perspective - p. 41 11. Chapter 3 Medical image analysis using swarm intelligence: A survey - p. 89 12. Chapter 4 An optimal and robust segmentation framework for analysis and detection of brain tumor in MRI images - p. 131 13. Chapter 5 Autoimmune diseases and an approach to type 1 diabetes analysis using PSO, K-means, and silhouette values - p. 159 14. Chapter 6 Consumer portfolio management with usage of evolutionary approach by PSO algorithm and machine learning models - p. 187 15. Chapter 7 Portfolio optimization using simulated annealing and quantum-inspired simulated annealing: A comparative study - p. 213 16. Chapter 8 Design of vehicular ad hoc network using IoT based on particle swarm optimization technique - p. 245 17. Chapter 9 Modeling and parametric optimization of grinding process using flower pollination algorithm - p. 265 18. Chapter 10 New quantum-inspired salp swarm algorithm: A comparative study on numerical computation - p. 291 19. Chapter 11 A fuzzy soft coronavirus alarm model - p. 331 20. Chapter 12 Conclusion and future research directions - p. 343 21. Index - p. 349
