Nature-Inspired Optimization Algorithms / Edition 2

by Xin-She Yang

Overview

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.

Product Details

ISBN-13:	9780128219867
Publisher:	Elsevier Science
Publication date:	09/14/2020
Edition description:	2nd ed.
Pages:	310
Sales rank:	1,019,096
Product dimensions:	7.50(w) x 9.25(h) x (d)

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 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).

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