Nature-inspired optimization algorithms /

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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 ca...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Yang, Xin-She (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London ; San Diego, CA : Academic Press, [2021]
Edición:Second edition.
Temas:
Mathematical optimization.
Nature-inspired algorithms.
Optimisation math�ematique.
Algorithmes inspir�es par la nature.
Acceso en línea:Texto completo

MARC

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100 1 |a Yang, Xin-She,  |e author. 
245 1 0 |a Nature-inspired optimization algorithms /  |c Xin-She Yang. 
250 |a Second edition. 
264 1 |a London ;  |a San Diego, CA :  |b Academic Press,  |c [2021] 
300 |a 1 online resource 
336 |a text  |2 rdacontent 
337 |a computer  |2 rdamedia 
338 |a online resource  |2 rdacarrier 
500 |a 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 Algorithms 10. Cuckoo Search 11. Bat Algorithms 12. Flower Pollination Algorithms 13. A Framework for Self-Tuning Algorithms 14. How to Deal With Constraints 15. Multi-Objective Optimization 16. Data Mining and Deep Learning Appendix A Test Function Benchmarks for Global Optimization Appendix B Matlab� Programs 
588 |a Description based on online resource; title from digital title page (viewed on December 23, 2020). 
520 |a 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. 
650 0 |a Mathematical optimization. 
650 0 |a Nature-inspired algorithms. 
650 6 |a Optimisation math�ematique.  |0 (CaQQLa)201-0007680 
650 6 |a Algorithmes inspir�es par la nature.  |0 (CaQQLa)000305079 
650 7 |a Mathematical optimization.  |2 fast  |0 (OCoLC)fst01012099 
650 7 |a Nature-inspired algorithms.  |2 fast  |0 (OCoLC)fst01986501 
776 0 8 |i Print version:  |z 9780128219867 
856 4 0 |u https://sciencedirect.uam.elogim.com/science/book/9780128219867  |z Texto completo 

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