
Swarm Intelligence and Bio-Inspired Computation
Theory and Applications
Description
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
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

Affiliations and Expertise
Zhihua Cui
Affiliations and Expertise
Renbin Xiao
Affiliations and Expertise
Amir Hossein Gandomi
Affiliations and Expertise
Mehmet Karamanoglu
Affiliations and Expertise
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