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Swarm Intelligence and Bio-Inspired Computation

Book 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.



  • 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.

Table of Contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. List of Contributors
  6. Preface
  7. Part One: Theoretical Aspects of Swarm Intelligence and Bio-Inspired Computing
    1. 1. Swarm Intelligence and Bio-Inspired Computation
      1. 1.1 Introduction
      2. 1.2 Current Issues in Bio-Inspired Computing
      3. 1.3 Search for the Magic Formulas for Optimization
      4. 1.4 Characteristics of Metaheuristics
      5. 1.5 Swarm-Intelligence-Based Algorithms
      6. 1.6 Open Problems and Further Research Topics
      7. References
    2. 2. Analysis of Swarm Intelligence–Based Algorithms for Constrained Optimization
      1. 2.1 Introduction
      2. 2.2 Optimization Problems
      3. 2.3 Swarm Intelligence–Based Optimization Algorithms
      4. 2.4 Numerical Examples
      5. 2.5 Summary and Conclusions
      6. References
    3. 3. Lévy Flights and Global Optimization
      1. 3.1 Introduction
      2. 3.2 Metaheuristic Algorithms
      3. 3.3 Lévy Flights in Global Optimization
      4. 3.4 Metaheuristic Algorithms Based on Lévy Probability Distribution: Is It a Good Idea?
      5. 3.5 Discussion
      6. 3.6 Conclusions
      7. References
    4. 4. Memetic Self-Adaptive Firefly Algorithm
      1. 4.1 Introduction
      2. 4.2 Optimization Problems and Their Complexity
      3. 4.3 Memetic Self-Adaptive Firefly Algorithm
      4. 4.4 Case Study: Graph 3-Coloring
      5. 4.5 Conclusions
      6. References
    5. 5. Modeling and Simulation of Ant Colony’s Labor Division
      1. 5.1 Introduction
      2. 5.2 Ant Colony’s Labor Division Behavior and its Modeling Description
      3. 5.3 Modeling and Simulation of Ant Colony’s Labor Division with Multitask
      4. 5.4 Modeling and Simulation of Ant Colony’s Labor Division with Multistate
      5. 5.5 Modeling and Simulation of Ant Colony’s Labor Division with Multiconstraint
      6. 5.6 Concluding Remarks
      7. Acknowledgment
      8. References
    6. 6. Particle Swarm Algorithm
      1. 6.1 Introduction
      2. 6.2 Convergence Analysis
      3. 6.3 Performance Illustration
      4. 6.4 Application in Hidden Markov Models
      5. 6.5 Conclusions
      6. References
    7. 7. A Survey of Swarm Algorithms Applied to Discrete Optimization Problems
      1. 7.1 Introduction
      2. 7.2 Swarm Algorithms
      3. 7.3 Main Concerns to Handle Discrete Problems
      4. 7.4 Applications to Discrete Problems
      5. 7.5 Discussion
      6. 7.6 Concluding Remarks and Future Research
      7. References
    8. 8. Test Functions for Global Optimization
      1. 8.1 Introduction
      2. 8.2 A Collection of Test Functions for GO
      3. 8.3 Conclusions
      4. References
  8. Part Two: Applications and Case Studies
    1. 9. Binary Bat Algorithm for Feature Selection
      1. 9.1 Introduction
      2. 9.2 Bat Algorithm
      3. 9.3 Binary Bat Algorithm
      4. 9.4 Optimum-Path Forest Classifier
      5. 9.5 Binary Bat Algorithm
      6. 9.6 Experimental Results
      7. 9.7 Conclusions
      8. References
    2. 10. Intelligent Music Composition
      1. 10.1 Introduction
      2. 10.2 Unsupervised Intelligent Composition
      3. 10.3 Supervised Intelligent Composition
      4. 10.4 Interactive Intelligent Composition
      5. 10.5 Conclusions
      6. References
    3. 11. A Review of the Development and Applications of the Cuckoo Search Algorithm
      1. 11.1 Introduction
      2. 11.2 Cuckoo Search Algorithm
      3. 11.3 Modifications and Developments
      4. 11.4 Applications
      5. 11.5 Conclusion
      6. References
    4. 12. Bio-Inspired Models for Semantic Web
      1. 12.1 Introduction
      2. 12.2 Semantic Web
      3. 12.3 Constituent Models
      4. 12.4 Neuro-Fuzzy System for the Web Content Filtering: Application
      5. 12.5 Conclusions
      6. References
    5. 13. Discrete Firefly Algorithm for Traveling Salesman Problem
      1. 13.1 Introduction
      2. 13.2 Evolutionary Discrete Firefly Algorithm
      3. 13.3 A New DFA for the TSP
      4. 13.4 Result and Discussion
      5. 13.5 Conclusion
      6. Acknowledgment
      7. References
    6. 14. Modeling to Generate Alternatives Using Biologically Inspired Algorithms
      1. 14.1 Introduction
      2. 14.2 Modeling to Generate Alternatives
      3. 14.3 FA for Function Optimization
      4. 14.4 FA-Based Concurrent Coevolutionary Computational Algorithm for MGA
      5. 14.5 Computational Testing of the FA Used for MGA
      6. 14.6 An SO Approach for Stochastic MGA
      7. 14.7 Case Study of Stochastic MGA for the Expansion of Waste Management Facilities
      8. 14.8 Conclusions
      9. References
    7. 15. Structural Optimization Using Krill Herd Algorithm
      1. 15.1 Introduction
      2. 15.2 Krill Herd Algorithm
      3. 15.3 Implementation and Numerical Experiments
      4. 15.4 Conclusions and Future Research
      5. References
    8. 16. Artificial Plant Optimization Algorithm
      1. 16.1 Introduction
      2. 16.2 Primary APOA
      3. 16.3 Standard APOA
      4. 16.4 Conclusion
      5. Acknowledgment
      6. References
    9. 17. Genetic Algorithm for the Dynamic Berth Allocation Problem in Real Time
      1. 17.1 Introduction
      2. 17.2 Literature Review
      3. 17.3 Optimization Model
      4. 17.4 Solution Procedure by Genetic Algorithm
      5. 17.5 Results and Analysis
      6. 17.6 Conclusion
      7. References
    10. 18. Opportunities and Challenges of Integrating Bio-Inspired Optimization and Data Mining Algorithms
      1. 18.1 Introduction
      2. 18.2 Challenges in Data Mining
      3. 18.3 Bio-Inspired Optimization Metaheuristics
      4. 18.4 The Convergence
      5. 18.5 Conclusion
      6. References
    11. 19. Improvement of PSO Algorithm by Memory-Based Gradient Search—Application in Inventory Management
      1. 19.1 Introduction
      2. 19.2 The Improved PSO Algorithm
      3. 19.3 Stochastic Optimization of Multiechelon Supply Chain Model
      4. 19.4 Conclusion
      5. Acknowledgment
      6. References