Swarm Intelligence and Bio-Inspired Computation

Swarm Intelligence and Bio-Inspired Computation

Theory and Applications

1st Edition - May 16, 2013

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  • Editors: Xin-She Yang, Zhihua Cui, Renbin Xiao, Amir Hossein Gandomi, Mehmet Karamanoglu
  • eBook ISBN: 9780124051775
  • Hardcover ISBN: 9780124051638

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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.

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.

Readership

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

Table of Contents

  • List of Contributors

    Preface

    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

    References

    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

    References

    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

    References

    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

    References

    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

    Acknowledgment

    References

    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

    References

    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

    References

    8. Test Functions for Global Optimization

    8.1 Introduction

    8.2 A Collection of Test Functions for GO

    8.3 Conclusions

    References

    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

    References

    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

    References

    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

    References

    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

    References

    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

    Acknowledgment

    References

    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

    References

    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

    References

    16. Artificial Plant Optimization Algorithm

    16.1 Introduction

    16.2 Primary APOA

    16.3 Standard APOA

    16.4 Conclusion

    Acknowledgment

    References

    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

    References

    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

    References

    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

    Acknowledgment

    References

Product details

  • No. of pages: 450
  • Language: English
  • Copyright: © Elsevier 2013
  • Published: May 16, 2013
  • Imprint: Elsevier
  • eBook ISBN: 9780124051775
  • Hardcover ISBN: 9780124051638

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

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.

Affiliations and Expertise

The University of Akron,USA

Mehmet Karamanoglu

Affiliations and Expertise

Middlesex University, London, UK

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