Nature-Inspired Optimization Algorithms

by Xin-She Yang

Overview

Nature-Inspired Optimization Algorithms 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.

Product Details

ISBN-13:	9780128219898
Publisher:	Elsevier Science & Technology Books
Publication date:	09/09/2020
Sold by:	Barnes & Noble
Format:	eBook
Pages:	310
File size:	15 MB
Note:	This product may take a few minutes to download.

About the Author

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).

Table of Contents

1. Introduction to Algorithms 2. Mathematical Foundations3. Analysis of Algorithms4. Random Walks and Optimization5. Simulated Annealing6. Genetic Algorithms7. Differential Evolution8. Particle Swarm Optimization9. Firefly Algorithms10. Cuckoo Search11. Bat Algorithms12. Flower Pollination Algorithms13. A Framework for Self-Tuning Algorithms14. How to Deal With Constraints15. Multi-Objective Optimization16. Data Mining and Deep LearningAppendix A Test Function Benchmarks for Global OptimizationAppendix B Matlab® Programs

What People are Saying About This

From the Publisher

A theoretical and practical introduction to all major nature-inspired algorithms for optimization

Editorial Reviews

"...the book is well written and easy to follow, even for algorithmic and mathematical laymen. Since the book focuses on optimization algorithms, it covers a very important and actual topic." —IEEE Communications Magazine, Nature-Inspired Optimization Algorithms

"...this book strives to introduce the latest developments regarding all major nature-inspired algorithms…" - HPCMagazine.com, August 2014

From the Publisher