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Author Yang, Xin-She, author.

Title Nature-inspired optimization algorithms / Xin-She Yang.

Publication Info. London ; San Diego, CA : Academic Press, [2021]


Location Call No. OPAC Message Status
 Axe Elsevier ScienceDirect Ebook  Electronic Book    ---  Available
Edition Second edition.
Description 1 online resource
text rdacontent
computer rdamedia
online resource rdacarrier
Note 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
Description based on online resource; title from digital title page (viewed on December 23, 2020).
Summary 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.
Subject Mathematical optimization.
Nature-inspired algorithms.
Optimisation mathématique.
Algorithmes inspirés par la nature.
Mathematical optimization. (OCoLC)fst01012099
Nature-inspired algorithms. (OCoLC)fst01986501
Other Form: Print version: 9780128219867
ISBN 9780128219898 electronic publication
0128219890 electronic publication
9780128219867 paperback
Standard No. UKMGB 019848851
AU@ 000068737195
AU@ 000068723631

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