Swarm Intelligence and Bio-Inspired Computation
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Swarm Intelligence and Bio-Inspired Computation : Theory and Applications

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Description

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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Product details

  • Hardback | 450 pages
  • 158 x 234 x 32mm | 759.99g
  • United States
  • English
  • black & white illustrations
  • 0124051634
  • 9780124051638
  • 1,763,378

Table of contents

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
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Review quote

"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
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About Amir Hossein Gandomi

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).
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