Nature-inspired computation and swarm intelligence have become popular and effective tools for solving problems in optimization, computational intelligence, soft computing and data science. Recently, the literature in the field has expanded rapidly, with new algorithms and applications emerging.
Nature-Inspired Computation and Swarm Intelligence: Algorithms, Theory and Applications is a timely reference giving a comprehensive review of relevant state-of-the-art developments in algorithms, theory and applications of nature-inspired algorithms and swarm intelligence. It reviews and documents the new developments, focusing on nature-inspired algorithms and their theoretical analysis, as well as providing a guide to their implementation. The book includes case studies of diverse real-world applications, balancing explanation of the theory with practical implementation.
Nature-Inspired Computation and Swarm Intelligence: Algorithms, Theory and Applications is suitable for researchers and graduate students in computer science, engineering, data science, and management science, who want a comprehensive review of algorithms, theory and implementation within the fields of nature inspired computation and swarm intelligence.
1. Cover image - p. i 2. Title page - p. ii 3. Table of Contents - p. iii 4. Copyright - p. iv 5. List of contributors - p. xv 6. About the editor - p. xix 7. Preface - p. xxi 8. Acknowledgments - p. xxiii 9. Part 1: Algorithms - p. xxiii 10. Chapter 1: Nature-inspired computation and swarm intelligence: a state-of-the-art overview - p. 3 11. Chapter 2: Bat algorithm and cuckoo search algorithm - p. 19 12. Chapter 3: Firefly algorithm and flower pollination algorithm - p. 35 13. Chapter 4: Bio-inspired algorithms: principles, implementation, and applications to wireless communication - p. 49 14. Part 2: Theory - p. 63 15. Chapter 5: Mathematical foundations for algorithm analysis - p. 67 16. Chapter 6: Probability theory for analyzing nature-inspired algorithms - p. 77 17. Chapter 7: Mathematical framework for algorithm analysis - p. 89 18. Part 3: Applications - p. 108 19. Chapter 8: Fine-tuning restricted Boltzmann machines using quaternion-based flower pollination algorithm - p. 111 20. Chapter 9: Traveling salesman problem: a perspective review of recent research and new results with bio-inspired metaheuristics - p. 135 21. Chapter 10: Clustering with nature-inspired metaheuristics - p. 165 22. Chapter 11: Bat-inspired algorithm for feature selection and white blood cell classification - p. 179 23. Chapter 12: Modular granular neural network optimization using the firefly algorithm applied to time series prediction - p. 199 24. Chapter 13: Artificial intelligence methods for music generation: a review and future perspectives - p. 217 25. Chapter 14: Optimized controller design for islanded microgrid employing nondominated sorting firefly algorithm - p. 247 26. Chapter 15: Swarm robotics – a case study: bat robotics - p. 273 27. Chapter 16: Electrical harmonics estimation in power systems using bat algorithm - p. 303 28. Chapter 17: CSBIIST: cuckoo search-based intelligent image segmentation technique - p. 323 29. Chapter 18: Improving genetic algorithm solution performance for optimal order allocation in an e-market with the Pareto-optimal set - p. 339 30. Chapter 19: Multirobot coordination through bio-inspired strategies - p. 361 31. Chapter 20: Optimization in probabilistic domains: an engineering approach - p. 391 32. Index - p. 415
Nature-inspired computation and swarm intelligence have become popular and effective tools for solving problems in optimization, computational intelligence, soft computing and data science. Recently, the literature in the field has expanded rapidly, with new algorithms and applications emerging.
Nature-Inspired Computation and Swarm Intelligence: Algorithms, Theory and Applications is a timely reference giving a comprehensive review of relevant state-of-the-art developments in algorithms, theory and applications of nature-inspired algorithms and swarm intelligence. It reviews and documents the new developments, focusing on nature-inspired algorithms and their theoretical analysis, as well as providing a guide to their implementation. The book includes case studies of diverse real-world applications, balancing explanation of the theory with practical implementation.
Nature-Inspired Computation and Swarm Intelligence: Algorithms, Theory and Applications is suitable for researchers and graduate students in computer science, engineering, data science, and management science, who want a comprehensive review of algorithms, theory and implementation within the fields of nature inspired computation and swarm intelligence.
1. Cover image - p. i 2. Title page - p. ii 3. Table of Contents - p. iii 4. Copyright - p. iv 5. List of contributors - p. xv 6. About the editor - p. xix 7. Preface - p. xxi 8. Acknowledgments - p. xxiii 9. Part 1: Algorithms - p. xxiii 10. Chapter 1: Nature-inspired computation and swarm intelligence: a state-of-the-art overview - p. 3 11. Chapter 2: Bat algorithm and cuckoo search algorithm - p. 19 12. Chapter 3: Firefly algorithm and flower pollination algorithm - p. 35 13. Chapter 4: Bio-inspired algorithms: principles, implementation, and applications to wireless communication - p. 49 14. Part 2: Theory - p. 63 15. Chapter 5: Mathematical foundations for algorithm analysis - p. 67 16. Chapter 6: Probability theory for analyzing nature-inspired algorithms - p. 77 17. Chapter 7: Mathematical framework for algorithm analysis - p. 89 18. Part 3: Applications - p. 108 19. Chapter 8: Fine-tuning restricted Boltzmann machines using quaternion-based flower pollination algorithm - p. 111 20. Chapter 9: Traveling salesman problem: a perspective review of recent research and new results with bio-inspired metaheuristics - p. 135 21. Chapter 10: Clustering with nature-inspired metaheuristics - p. 165 22. Chapter 11: Bat-inspired algorithm for feature selection and white blood cell classification - p. 179 23. Chapter 12: Modular granular neural network optimization using the firefly algorithm applied to time series prediction - p. 199 24. Chapter 13: Artificial intelligence methods for music generation: a review and future perspectives - p. 217 25. Chapter 14: Optimized controller design for islanded microgrid employing nondominated sorting firefly algorithm - p. 247 26. Chapter 15: Swarm robotics – a case study: bat robotics - p. 273 27. Chapter 16: Electrical harmonics estimation in power systems using bat algorithm - p. 303 28. Chapter 17: CSBIIST: cuckoo search-based intelligent image segmentation technique - p. 323 29. Chapter 18: Improving genetic algorithm solution performance for optimal order allocation in an e-market with the Pareto-optimal set - p. 339 30. Chapter 19: Multirobot coordination through bio-inspired strategies - p. 361 31. Chapter 20: Optimization in probabilistic domains: an engineering approach - p. 391 32. Index - p. 415
