Nature-Inspired Optimization Algorithms, Second Edition provides a systematic 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 well-chosen 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, as well as multi-objective optimization. This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It can also serve a source of inspiration for new applications. Researchers and engineers as well as experienced experts will also find it a handy reference. In the last few years, there are some signi?cant developments concerning nature-inspired optimization algorithms, their variants and applications. More applications have been carried out in a wide range of realworld settings. This Second Edition with new updates and additions, re?ects the latest state-of-the-art developments, including more details about the background and mathematical foundations of these algorithms. Furthermore, the new edition shows how such new optimization techniques can be linked to other active research areas such as data mining, machine learning and deep learning. The Second Edition includes four new chapters, including a new Chapter 2 to introduce the mathematical foundations so as to help readers to gain greater insight into algorithms, a new Chapter 15 to introduce techniques for solving discrete and combination optimization problems, a new Chapter 18 introduces data mining techniques and their links to optimization algorithms, and a new Chapter 19 introduces the latest deep learning techniques, background and various applications. Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literatureProvides a theoretical understanding as well as practical implementation hintsProvides a step-by-step introduction to each algorithmThe Second Edition includes four new chapters covering mathematical foundations, techniques for solving discrete and combination optimization problems, data mining techniques and their links to optimization algorithms, as well as the latest deep learning techniques, background and various applications INDICE: 1. Introduction to Algorithms 2. Mathematical Foundations 3. Analysis of Algorithms 4. Random Walks and Optimization 5. Simulated Annealing 6 Genetic Algorithms 7. Differential Evolution 8. Particle Swarm Optimization 9. Firefly Algorithm 10. Cuckoo Search 11. Bat Algorithm 12. Flower Pollination Algorithms 13. A Framework for Self-Tuning Algorithms 14. How to Deal with Constraints 15. Discrete and Combination Optimization 16. Multiobjective Optimization 17. Other Algorithms and Hybrid Algorithms 18. Optimization and Data Mining 19. Optimization and Deep Learning Appendix A. Test Function Benchmarks for Global Optimization B. Matlab Programs
- ISBN: 978-0-12-821986-7
- Editorial: Academic Press
- Encuadernacion: Rústica
- Páginas: 332
- Fecha Publicación: 01/09/2020
- Nº Volúmenes: 1
- Idioma: Inglés