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Swarm Intelligence and Bio-Inspired Computation - 1st Edition - ISBN: 9780124051638, 9780124051775

Swarm Intelligence and Bio-Inspired Computation

1st Edition

Theory and Applications

Editors: Xin-She Yang Zhihua Cui Renbin Xiao Amir Hossein Gandomi Mehmet Karamanoglu
Hardcover ISBN: 9780124051638
eBook ISBN: 9780124051775
Imprint: Elsevier
Published Date: 20th May 2013
Page Count: 450
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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.

Key Features

  • Focuses on the introduction and analysis of key algorithms
  • Includes case studies for real-world applications
  • Contains a balance of theory and applications, so readers who are interested in either algorithm or applications will all benefit from this timely book.


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

Table of Contents

List of Contributors


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

1. Swarm Intelligence and Bio-Inspired Computation

1.1 Introduction

1.2 Current Issues in Bio-Inspired Computing

1.3 Search for the Magic Formulas for Optimization

1.4 Characteristics of Metaheuristics

1.5 Swarm-Intelligence-Based Algorithms

1.6 Open Problems and Further Research Topics


2. Analysis of Swarm Intelligence–Based Algorithms for Constrained Optimization

2.1 Introduction

2.2 Optimization Problems

2.3 Swarm Intelligence–Based Optimization Algorithms

2.4 Numerical Examples

2.5 Summary and Conclusions


3. Lévy Flights and Global Optimization

3.1 Introduction

3.2 Metaheuristic Algorithms

3.3 Lévy Flights in Global Optimization

3.4 Metaheuristic Algorithms Based on Lévy Probability Distribution: Is It a Good Idea?

3.5 Discussion

3.6 Conclusions


4. Memetic Self-Adaptive Firefly Algorithm

4.1 Introduction

4.2 Optimization Problems and Their Complexity

4.3 Memetic Self-Adaptive Firefly Algorithm

4.4 Case Study: Graph 3-Coloring

4.5 Conclusions


5. Modeling and Simulation of Ant Colony’s Labor Division

5.1 Introduction

5.2 Ant Colony’s Labor Division Behavior and its Modeling Description

5.3 Modeling and Simulation of Ant Colony’s Labor Division with Multitask

5.4 Modeling and Simulation of Ant Colony’s Labor Division with Multistate

5.5 Modeling and Simulation of Ant Colony’s Labor Division with Multiconstraint

5.6 Concluding Remarks



6. Particle Swarm Algorithm

6.1 Introduction

6.2 Convergence Analysis

6.3 Performance Illustration

6.4 Application in Hidden Markov Models

6.5 Conclusions


7. A Survey of Swarm Algorithms Applied to Discrete Optimization Problems

7.1 Introduction

7.2 Swarm Algorithms

7.3 Main Concerns to Handle Discrete Problems

7.4 Applications to Discrete Problems

7.5 Discussion

7.6 Concluding Remarks and Future Research


8. Test Functions for Global Optimization

8.1 Introduction

8.2 A Collection of Test Functions for GO

8.3 Conclusions


Part Two: Applications and Case Studies

9. Binary Bat Algorithm for Feature Selection

9.1 Introduction

9.2 Bat Algorithm

9.3 Binary Bat Algorithm

9.4 Optimum-Path Forest Classifier

9.5 Binary Bat Algorithm

9.6 Experimental Results

9.7 Conclusions


10. Intelligent Music Composition

10.1 Introduction

10.2 Unsupervised Intelligent Composition

10.3 Supervised Intelligent Composition

10.4 Interactive Intelligent Composition

10.5 Conclusions


11. A Review of the Development and Applications of the Cuckoo Search Algorithm

11.1 Introduction

11.2 Cuckoo Search Algorithm

11.3 Modifications and Developments

11.4 Applications

11.5 Conclusion


12. Bio-Inspired Models for Semantic Web

12.1 Introduction

12.2 Semantic Web

12.3 Constituent Models

12.4 Neuro-Fuzzy System for the Web Content Filtering: Application

12.5 Conclusions


13. Discrete Firefly Algorithm for Traveling Salesman Problem

13.1 Introduction

13.2 Evolutionary Discrete Firefly Algorithm

13.3 A New DFA for the TSP

13.4 Result and Discussion

13.5 Conclusion



14. Modeling to Generate Alternatives Using Biologically Inspired Algorithms

14.1 Introduction

14.2 Modeling to Generate Alternatives

14.3 FA for Function Optimization

14.4 FA-Based Concurrent Coevolutionary Computational Algorithm for MGA

14.5 Computational Testing of the FA Used for MGA

14.6 An SO Approach for Stochastic MGA

14.7 Case Study of Stochastic MGA for the Expansion of Waste Management Facilities

14.8 Conclusions


15. Structural Optimization Using Krill Herd Algorithm

15.1 Introduction

15.2 Krill Herd Algorithm

15.3 Implementation and Numerical Experiments

15.4 Conclusions and Future Research


16. Artificial Plant Optimization Algorithm

16.1 Introduction

16.2 Primary APOA

16.3 Standard APOA

16.4 Conclusion



17. Genetic Algorithm for the Dynamic Berth Allocation Problem in Real Time

17.1 Introduction

17.2 Literature Review

17.3 Optimization Model

17.4 Solution Procedure by Genetic Algorithm

17.5 Results and Analysis

17.6 Conclusion


18. Opportunities and Challenges of Integrating Bio-Inspired Optimization and Data Mining Algorithms

18.1 Introduction

18.2 Challenges in Data Mining

18.3 Bio-Inspired Optimization Metaheuristics

18.4 The Convergence

18.5 Conclusion


19. Improvement of PSO Algorithm by Memory-Based Gradient Search—Application in Inventory Management

19.1 Introduction

19.2 The Improved PSO Algorithm

19.3 Stochastic Optimization of Multiechelon Supply Chain Model

19.4 Conclusion




No. of pages:
© Elsevier 2013
20th May 2013
Hardcover ISBN:
eBook ISBN:

About the Editors

Xin-She Yang

Xin-She Yang

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).

Affiliations and Expertise

School of Science and Technology, Middlesex University, UK

Zhihua Cui

Affiliations and Expertise

Taiyuan University of Science and Technology, Shanxi, China

Renbin Xiao

Affiliations and Expertise

Huazhong University of Science and Technology, Wuhan, China

Amir Hossein Gandomi

Affiliations and Expertise

The University of Akron,USA

Mehmet Karamanoglu

Affiliations and Expertise

Middlesex University, London, UK


"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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