Name: Swarm Intelligence and Bio-Inspired Computation By Xin-She Yang (School of Science and Technology, Middlesex University, UK)
SKU: 9780124051638
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Swarm Intelligence and Bio-Inspired Computation Summary

Swarm Intelligence and Bio-Inspired Computation: Theory and Applications by Xin-She Yang (School of Science and Technology, Middlesex University, UK)

Swarm Intelligence and bio-inspired computation have become increasing popular in the last two decades. Bio-inspired algorithms such as ant colony algorithms, bat algorithms, bee algorithms, firefly algorithms, cuckoo search and particle swarm optimization have been applied in almost every area of science and engineering with a dramatic increase of number of relevant publications. This book reviews the latest developments in swarm intelligence and bio-inspired computation from both the theory and application side, providing a complete resource that analyzes and discusses the latest and future trends in research directions. It can help new researchers to carry out timely research and inspire readers to develop new algorithms. With its impressive breadth and depth, this book will be useful for advanced undergraduate students, PhD students and lecturers in computer science, engineering and science as well as researchers and engineers.

Swarm Intelligence and Bio-Inspired Computation Reviews

"Civil and other engineers, mathematicians, computer scientists, and other contributors summarize the current status of biologically inspired computation and swarm intelligence, looking at both fundamentals and applications of algorithms based on swarm intelligence and other biological systems." --Reference and Research Book News, August 2013

About Xin-She Yang (School of Science and Technology, Middlesex University, UK)

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). Dr. Amir H. Gandomi is anARC DECRA Fellow at the Faculty of Engineering andInformation Technology, University of Technology Sydney, Australia. Prior to joining UTS, Dr. Gandomi was an Assistant Professor at Stevens Institute of Technology, USA and a Distinguished Research Fellow in BEACON center, Michigan State University, USA. Dr. Gandomi has published over two hundred journal papers and seven books which collectively have been cited 19,000+ times. He has been named as one of the most influential scientific mindsand Highly Cited Researcher (top 1% publications and 0.1% researchers) for four consecutive years, 2017 to 2020. He also ranked 18th in GP bibliography among more than 12,000 researchers. He has served as associate editor, editor and guest editor in several prestigious journals such as AE of SWEVO, IEEE TBD, and IEEE IoTJ. Dr. Gandomi is active in delivering keynotes and invited talks. His research interests are global optimization andbigdata analytics using Machine Learning and evolutionary computations in particular.

1. Swarm Intelligence and Bio-Inspired Computation: An Overview 2. Review and Analysis of Swarm-intelligence Based Algorithms 3. Levy Flights and Global Optimization 4. Self-Adaptive Memetic Firefly Algorithm 5. Modelling and Simulation of Labor Division in An Ant Colony: A Problem-Oriented Approach 6. Particle Swarm Optimization and Their Variants: Convergence and Applications 7. A Survey of Swarm Algorithms Applied to Discrete Optimization Problems 8. A Comprehensive Survey of Test Functions for Global Optimization 9. Binary Bat Algorithm for Feature Selection 10. Intelligent Music Composition 11. The Development and Applications of the Cuckoo Search Algorithm 12. Bio-Inspired Models and the Semantic Web 13. Discrete Firefly Algorithm for Travelling Salesman Problem: A New Movement Scheme 14. Modelling to Generate Alternatives Using Biologically-Inspired Algorithms 15. Structural Optimization Using Krill Herd Algorithm 16. Artificial Plant Optimization Algorithm 17. Genetic Algorithms for the Berth Allocation Problem in Real Time 18. Opportunities and Challenges of Integrating Bio-Inspired Optimization and Data Mining Algorithms 19. Improvement of PSO Algorithm by Memory Based Gradient Search: Application in Inventory Management

Additional information

Sku

NPB9780124051638

ISBN 13

9780124051638

ISBN 10

0124051634

Title

Swarm Intelligence and Bio-Inspired Computation: Theory and Applications by Xin-She Yang (School of Science and Technology, Middlesex University, UK)

Author

Xin-She Yang (School of Science and Technology, Middlesex University, UK)

Condition

New

Binding type

Hardback

Publisher

Elsevier Science Publishing Co Inc

Year published

2013-05-20

Number of pages

450

Prizes

N/A

Cover note

Book picture is for illustrative purposes only, actual binding, cover or edition may vary.

Note

This is a new book - be the first to read this copy. With untouched pages and a perfect binding, your brand new copy is ready to be opened for the first time

Swarm Intelligence and Bio-Inspired Computation Xin-She Yang (School of Science and Technology, Middlesex University, UK)

Swarm Intelligence and Bio-Inspired Computation by Xin-She Yang (School of Science and Technology, Middlesex University, UK)

Summary

Swarm Intelligence and Bio-Inspired Computation Summary

Swarm Intelligence and Bio-Inspired Computation: Theory and Applications by Xin-She Yang (School of Science and Technology, Middlesex University, UK)

Swarm Intelligence and Bio-Inspired Computation Reviews

About Xin-She Yang (School of Science and Technology, Middlesex University, UK)

Table of Contents

Additional information

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