Swarm Intelligence and Bio-Inspired Computation

Theory and Applications

Edited by

  • Xin-She Yang, School of Science and Technology, Middlesex University, UK
  • Zhihua Cui, Taiyuan University of Science and Technology, Shanxi, China
  • Renbin Xiao, Huazhong University of Science and Technology, Wuhan, China
  • Amir Hossein Gandomi, The University of Akron,USA
  • Mehmet Karamanoglu, Middlesex University, London, 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.
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Advanced students and researchers in computer science, engineering and applied mathematics.


Book information

  • Published: May 2013
  • Imprint: ELSEVIER
  • ISBN: 978-0-12-405163-8


"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

Table of Contents

Part One: Theoretical Aspects of Swarm Intelligence and Bio-Inspired Computing

1. Swarm Intelligence and Bio-Inspired Computation: An Overview (Xin-She Yang and Mehmet Karamanoglu)

2. Review and Analysis of Swarm-intelligence Based Algorithms (M. P. Saka and E. Doğan and I. Aydogdu)

3. Lévy Flights and Global Optimization (Momin Jamil and Hans-Jurgen Zepernik)

4. Self-Adaptive Memetic Firefly Algorithm (Iztok Fister, Xin-She Yang, Janez Brest and Iztok Jr. Fister)

5. Modelling and Simulation of Labor Division in An Ant Colony: A Problem-Oriented Approach (Renbin Xiao )

6. Particle Swarm Optimization and Their Variants: Convergence and Applications (Shichang Sun and Hongbo Liu)

7. A Survey of Swarm Algorithms Applied to Discrete Optimization Problems (Rafael Parpinelli, Heitor Silverio Lopes, Jonas Krause and Jelson Cordeiro)

8. A Comprehensive Survey of Test Functions for Global Optimization (Momin Jamil, Xin-She Yang and Hans-Jurgen Zepernik)

Part Two: Applications and Case Studies

9. Binary Bat Algorithm for Feature Selection (Rodrigo Nakamura, Luis Pereira, Kelton Costa, João Paulo Papa, and Xin-She Yang)

10. Intelligent Music Composition (Maximos Kaliakatsos-Papakostas , Andreas Floros and Michael N. Vrahatis)

11. The Development and Applications of the Cuckoo Search Algorithm (Sean Walton, Oubay Hassan, Kenneth Morgan, and Rowan Brown)

12. Bio-Inspired Models and the Semantic Web (Priti Sajja and Rajendra Akerkar)

13. Discrete Firefly Algorithm for Travelling Salesman Problem: A New Movement Scheme (Gilang Kusuma Jati, Ruli Manurung and Suyanto Suyanto)

14. Modelling to Generate Alternatives Using Biologically-Inspired Algorithms (Raha Imanirad and Julian Scott Yeomans)

15. Structural Optimization Using Krill Herd Algorithm (Amir Hossein Gandomi and Amir H. Alavi)

16. Artificial Plant Optimization Algorithm (Zhihua Cui and Xingjuan Cai)

17. Genetic Algorithms for the Berth Allocation Problem in Real Time (Carlos Arango, Pablo Cortes, A. Escudero and Luis Onieva)

18. Opportunities and Challenges of Integrating Bio-Inspired Optimization and Data Mining Algorithms (Simon Fong)

19. Improvement of PSO Algorithm by Memory Based Gradient Search: Application in Inventory Management (Tamás Varga, András Király and Janos Abonyi)