Nature-Inspired Optimization Algorithms, Second Edition provides an introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, and multi-objective optimization. This book can serve as an introductory book for graduates, for lecturers in computer science, engineering and natural sciences, and as a source of inspiration for new applications.
1. Chapter 1 : Introduction to Algorithms - p. Page xi 2. Chapter 2 : Mathematical Foundations - p. Page 21 3. Chapter 3 : Analysis of Algorithms - p. Page 38 4. Chapter 4 : Random Walks and Optimization - p. Page 57 5. Chapter 5 : Simulated Annealing - p. Page 80 6. Chapter 6 : Genetic Algorithms - p. Page 90 7. Chapter 7 : Differential Evolution - p. Page 100 8. Chapter 8 : Particle Swarm Optimization - p. Page 109 9. Chapter 9 : Firefly Algorithms - p. Page 121 10. Chapter 10 : Cuckoo Search - p. Page 138 11. Chapter 11 : Bat Algorithms - p. Page 153 12. Chapter 12 : Flower Pollination Algorithms - p. Page 171 13. Chapter 13 : A Framework for Self-Tuning Algorithms - p. Page 193 14. Chapter 14 : How to Deal With Constraints - p. Page 204 15. Chapter 15 : Multi-Objective Optimization - p. Page 218 16. Chapter 16 : Data Mining and Deep Learning - p. Page 236 17. Appendix A : Test Function Benchmarks for Global Optimization - p. Page 256 18. Appendix B : Matlab® Programs - p. Page 275 19. Index - p. Page 288
Nature-Inspired Optimization Algorithms, Second Edition provides an introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, and multi-objective optimization. This book can serve as an introductory book for graduates, for lecturers in computer science, engineering and natural sciences, and as a source of inspiration for new applications.
1. Chapter 1 : Introduction to Algorithms - p. Page xi 2. Chapter 2 : Mathematical Foundations - p. Page 21 3. Chapter 3 : Analysis of Algorithms - p. Page 38 4. Chapter 4 : Random Walks and Optimization - p. Page 57 5. Chapter 5 : Simulated Annealing - p. Page 80 6. Chapter 6 : Genetic Algorithms - p. Page 90 7. Chapter 7 : Differential Evolution - p. Page 100 8. Chapter 8 : Particle Swarm Optimization - p. Page 109 9. Chapter 9 : Firefly Algorithms - p. Page 121 10. Chapter 10 : Cuckoo Search - p. Page 138 11. Chapter 11 : Bat Algorithms - p. Page 153 12. Chapter 12 : Flower Pollination Algorithms - p. Page 171 13. Chapter 13 : A Framework for Self-Tuning Algorithms - p. Page 193 14. Chapter 14 : How to Deal With Constraints - p. Page 204 15. Chapter 15 : Multi-Objective Optimization - p. Page 218 16. Chapter 16 : Data Mining and Deep Learning - p. Page 236 17. Appendix A : Test Function Benchmarks for Global Optimization - p. Page 256 18. Appendix B : Matlab® Programs - p. Page 275 19. Index - p. Page 288
