Nature-Inspired Optimization Algorithms
eBook - ePub

Nature-Inspired Optimization Algorithms

Xin-She Yang

  1. 310 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Nature-Inspired Optimization Algorithms

Xin-She Yang

Book details
Table of contents
Citations

About This Book

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.

  • Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literature
  • Provides a theoretical understanding and practical implementation hints
  • Presents a step-by-step introduction to each algorithm
  • Includes four new chapters covering mathematical foundations, techniques for solving discrete and combination optimization problems, data mining techniques and their links to optimization algorithms, and the latest deep learning techniques, background and various applications

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Information

Publisher
Academic Press
Year
2020
ISBN
9780128219898
Edition
2
Topic
Biological Sciences
Subtopic
Biotechnology
Index
Biological Sciences

Table of contents

    Citation styles for Nature-Inspired Optimization Algorithms

    APA 6 Citation

    Yang, X.-S. (2020). Nature-Inspired Optimization Algorithms (2nd ed.). Academic Press. Retrieved from https://www.perlego.com/book/1809376 (Original work published 2020)

    Chicago Citation

    Yang, Xin-She. (2020) 2020. Nature-Inspired Optimization Algorithms. 2nd ed. Academic Press. https://www.perlego.com/book/1809376.

    Harvard Citation

    Yang, X.-S. (2020) Nature-Inspired Optimization Algorithms. 2nd edn. Academic Press. Available at: https://www.perlego.com/book/1809376 (Accessed: 3 July 2024).

    MLA 7 Citation

    Yang, Xin-She. Nature-Inspired Optimization Algorithms. 2nd ed. Academic Press, 2020. Web. 3 July 2024.