Swarm Intelligence and Bio-Inspired Computation

Theory and Applications
Erscheinungsjahr 2013
ISBN: 978-0-12-405163-8
Verlag: Elsevier Science Publishing Co Inc

Buch, Englisch, 450 Seiten, Format (B × H): 156 mm x 236 mm, Gewicht: 766 g

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.

Zielgruppe

Advanced students and researchers in computer science, engineering and applied mathematics.

Autoren/Hrsg.

Yang, Xin-She

Cui, Zhihua

Xiao, Renbin

Gandomi, Amir Hossein

Karamanoglu, Mehmet

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Technische Wissenschaften Technik Allgemein Bionik, Biomimetik

Inhaltsverzeichnis

1. Swarm Intelligence and Bio-Inspired Computation: An Overview

2. Review and Analysis of Swarm-intelligence Based Algorithms

3. Lévy 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

Über Autor(innen)

Yang, Xin-She
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).

Gandomi, Amir Hossein
Amir H. Gandomi, PhD, is a leading researcher in global optimization and big data analytics, currently serving as a Professor of Data Science and an ARC DECRA Fellow at the University of Technology Sydney (UTS). With over 450 journal publications and 60,000 citations, he is among the most cited researchers worldwide. Dr. Gandomi has authored 14 books and received numerous accolades, including the IEEE TCSC Award and the Achenbach Medal. His editorial roles span several prestigious journals, and he is a sought-after keynote speaker in the fields of artificial intelligence and genetic programming. Previously, he held academic positions at the Stevens Institute of Technology and Michigan State University, where he contributed significantly to advancing knowledge in machine learning and evolutionary computation.