Nature-Inspired Optimization Algorithms - Softcover

Über die Autorin bzw. den Autor

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 in Modelling and Simulation at Middlesex University London, Fellow of the Institute of Mathematics and its Application (IMA) and a Book Series Co-Editor of the Springer Tracts in Nature-Inspired Computing. He has published more than 25 books and more than 400 peer-reviewed research publications with over 82000 citations, and he has been on the prestigious list of highly cited researchers (Web of Sciences) for seven consecutive years (2016-2022).

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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.