Swarm Intelligence and Bio-Inspired Computation

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.

• 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.

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