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Swarm Intelligence and Bio-Inspired Computation
Theory and Applications
- 1st Edition - May 16, 2013
- Editors: Xin-She Yang, Zhihua Cui, Renbin Xiao, Amir Hossein Gandomi, Mehmet Karamanoglu
- Language: English
- Paperback ISBN:9 7 8 - 1 - 4 9 3 3 - 0 1 3 6 - 2
- Hardback ISBN:9 7 8 - 0 - 1 2 - 4 0 5 1 6 3 - 8
- eBook ISBN:9 7 8 - 0 - 1 2 - 4 0 5 1 7 7 - 5
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, be… Read more
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Request a sales quoteSwarm 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
- No. of pages: 450
- Language: English
- Edition: 1
- Published: May 16, 2013
- Imprint: Elsevier
- Paperback ISBN: 9781493301362
- Hardback ISBN: 9780124051638
- eBook ISBN: 9780124051775
XY
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 in Modelling and Simulation at Middlesex University London, Fellow of the Institute of Mathematics and its Application (IMA) and a Book Series Co-Editor of the Springer Tracts in Nature-Inspired Computing. He has published more than 25 books and more than 400 peer-reviewed research publications with over 82000 citations, and he has been on the prestigious list of highly cited researchers (Web of Sciences) for seven consecutive years (2016-2022).
Affiliations and expertise
School of Science and Technology, Middlesex University, UKZC
Zhihua Cui
Affiliations and expertise
Taiyuan University of Science and Technology, Shanxi, ChinaRX
Renbin Xiao
Affiliations and expertise
Huazhong University of Science and Technology, Wuhan, ChinaAG
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
University of Technology Sydney, AustraliaMK
Mehmet Karamanoglu
Affiliations and expertise
Middlesex University, London, UKRead Swarm Intelligence and Bio-Inspired Computation on ScienceDirect