Nature-Inspired Optimization Algorithms

Nature-Inspired Optimization Algorithms

2nd Edition - September 9, 2020

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  • Author: Xin-She Yang
  • Paperback ISBN: 9780128219867
  • eBook ISBN: 9780128219898

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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 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.

Key Features

  • 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


Graduates, PhD students and lecturers in computer science, engineering and natural sciences and also researchers and biomedical engineers

Table of Contents

  • 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

Product details

  • No. of pages: 310
  • Language: English
  • Copyright: © Academic Press 2020
  • Published: September 9, 2020
  • Imprint: Academic Press
  • Paperback ISBN: 9780128219867
  • eBook ISBN: 9780128219898

About the Author

Xin-She Yang

Xin-She Yang
Xin-She Yang obtained his DPhil in Applied Mathematics from the University of Oxford. He then worked at Cambridge University and National Physical Laboratory (UK) as a Senior Research Scientist. He is currently a Reader at Middlesex University London, Adjunct Professor at Reykjavik University (Iceland) and Guest Professor at Xi’an Polytechnic University (China). He is an elected Bye-Fellow at Downing College, Cambridge University. He is also the IEEE CIS Chair for the Task Force on Business Intelligence and Knowledge Management, and the Editor-in-Chief of International Journal of Mathematical Modelling and Numerical Optimisation (IJMMNO).

Affiliations and Expertise

School of Science and Technology, Middlesex University, UK

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