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Innovations and Developments of Swarm Intelligence Applications

Book Description

The natural social behavior of large groups of animals, such as flocks of birds, schools of fish, or colonies of ants has fascinated scientists for hundreds of years, if not longer, due to the intricate nature of their interactions and their ability to move and work together seemingly effortlessly.<br><b>Innovations and Developments of Swarm Intelligence Applications</b> explores the emerging realm of swarm intelligence, which finds its basis in the natural social behavior of animals. The study and application of this swarm behavior has led scientists to a new world of research as ways are found to apply this behavior to independent intelligent agents, creating complex solutions for real world applications. Worldwide contributions have been seamlessly combined in this comprehensive reference, providing a wealth of new information for researchers, academicians, students, and engineers.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Editorial Advisory Board and List of Reviewers
    1. Editorial Advisory Board
  5. Preface
    1. WHAT IS THE BOOK ABOUT?
    2. ORGANIZATION OF THE BOOK
  6. Acknowledgment
  7. Section 1: PSO Algorithms
    1. Chapter 1: Beyond Standard Particle Swarm Optimisation
      1. ABSTRACT
      2. TWO FOR ONE
      3. FOUR ISSUES ABOUT SPSO
      4. DISCUSSIONS AND SOLUTIONS
      5. TOWARDS A NEW FLEXIBLE STANDARD?
      6. APPENDIX: A MINI-BENCHMARK
    2. Chapter 2: Biases in Particle Swarm Optimization
      1. ABSTRACT
      2. INTRODUCTION
      3. BIAS PARALLEL TO THE COORDINATE AXES
      4. THEORETICAL EXPLANATION
      5. RESULTS WITH AN ELLIPSE
      6. RESULTS WITH AN ELLIPSE WITH A MINESHAFT
      7. CONCLUSION
      8. ACKNOWLEDGMENT
      9. APPENDIX A
      10. APPENDIX B
    3. Chapter 3: Taguchi-Particle Swarm Optimization for Numerical Optimization
      1. ABSTRACT
      2. INTRODUCTION
      3. THE CONCEPT OF TAGUCHI METHOD
      4. OVERVIEW OF PARTICLE SWARM OPTIMIZATION
      5. TAGUCHI-PARTICLE SWARM OPTIMIZATION
      6. EXPERIMENT SETTINGS
      7. RESULTS
      8. CONCLUSION
    4. Chapter 4: Constraint Handling in Particle Swarm Optimization
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED WORKS
      4. PROPOSED ALGORITHM
      5. SIMULATION STUDY
      6. CONCLUSION AND DISCUSSION
    5. Chapter 5: Adaptive Neuro-Fuzzy Control Approach Based on Particle Swarm Optimization
      1. ABSTRACT
      2. INTRODUCTION
      3. SYSTEM DESCRIPTION
      4. MODIFIED PARTICLE SWARM
      5. ADAPTIVE NEURO-FUZZY LOGIC IMPLEMENTATION
      6. ADAPTIVE NEURO-FUZZY LOGIC CONTROL IMPLEMENTATION
      7. CONCLUSION
    6. Chapter 6: Design of Multi-Criteria PI Controller Using Particle Swarm Optimization for Multiple UAVs Close Formation
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. MODEL OF MULTIPLE UAVS CLOSE FORMATION
      4. 3. CONTROLLER DESIGN
      5. 4. MULTI-CRITERIA PSO-BASED PI CONTROL DESIGN
      6. 6. CONCLUDING REMARKS
    7. Chapter 7: Oscillation Damping Enhancement via Coordinated Design of PSS and FACTS-Based Stabilizers in a Multi-Machine Power System Using PSO
      1. ABSTRACT
      2. INTRODUCTION
      3. SYSTEM MODELING
      4. PARTICLE SWARM OPTIMIZATION ALGORITHM
      5. PROPOSED DESIGN APPROACH
      6. SIMULATION RESULTS
      7. CONCLUSION
      8. APPENDIX
    8. Chapter 8: Compensation of Voltage Sags with Phase-Jumps through DVR with Minimum VA Rating Using PSO based ANFIS Controller
      1. ABSTRACT
      2. INTRODUCTION
      3. DYNAMIC VOLTAGE RESTORER
      4. DVR VA RATING MINIMIZATION
      5. PARTICLE SWARM OPTIMIZATION (PSO)
      6. ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS)
      7. STATE SPACE MODEL OF DVR
      8. SIMULATION STUDIES
      9. CONCLUSION
    9. Chapter 9: Optimal Power Flow with TCSC and TCPS Modeling using Craziness and Turbulent Crazy Particle Swarm Optimization
      1. ABSTRACT
      2. INTRODUCTION
      3. STATIC MODEL OF FACTS DEVICES
      4. ALGORITHMS
      5. SIMULATION RESULTS AND DISCUSSIONS
      6. CONCLUSION
    10. Chapter 10: Congestion Management Using Hybrid Particle Swarm Optimization Technique
      1. ABSTRACT
      2. INTRODUCTION
      3. PROBLEM FORMULATION
      4. PROPOSED HYBRID PARTICLE SWARM OPTIMIZATION TECHNIQUE
      5. PSUEDO CODE
      6. CONGESTION MANAGEMENT USING HPSO
      7. CONCLUSION
      8. APPENDIX
    11. Chapter 11: Particle Swarm Optimization Algorithms Inspired by Immunity-Clonal Mechanism and Their Applications to Spam Detection
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED WORKS
      4. CLONAL PARTICLE SWARM OPTIMIZATION
      5. TWO VARIANTS OF CPSO
      6. EXPERIMENTS AND ANALYSIS
      7. SPAM DETECTION APPLICATION
      8. CONCLUSION
  8. Section 2: Other Algorithms
    1. Chapter 12: Unit Commitment by Evolving Ant Colony Optimization
      1. ABSTRACT
      2. INTRODUCTION
      3. PROBLEM FORMULATION
      4. IMPLEMENTATION OF THE PROPOSED METHOD
      5. WORKING OF EACO
      6. SIMULATION RESULTS
      7. CONCLUSION
      8. NOMENCLATURE
    2. Chapter 13: Bacterial Foraging Optimization
      1. ABSTRACT
      2. 1 INTRODUCTION: BACTERIAL FORAGING: E. COLI
      3. 2 E. COLI BACTERIAL SWARM FORAGING FOR OPTIMIZATION
      4. 3 CONCLUSION: BFO APPLICATIONS AND DIRECTIONS
    3. Chapter 14: Networks Do Matter
      1. ABSTRACT
      2. INTRODUCTION
      3. DESIGNING THE DRIVER CONTROL STATE MACHINE
      4. THE CULTURAL ALGORITHM LEARNING PHASE
      5. TRAINING THE CONTROLLER
      6. CONCLUSION
    4. Chapter 15: Honey Bee Swarm Cognition
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. NEST-SITE SELECTION BY HONEY BEES
      4. 3. DISCRIMINATION AND DISTRACTION PROPERTIES
      5. 4. DISCRIMINATION-DISTRACTION INTERACTIONS
      6. 5. ADAPTIVE TUNING OF SWARM COGNITION PROCESSES
      7. 6. CONCLUSION
    5. Chapter 16: A Theoretical Framework for Estimating Swarm Success Probability Using Scouts
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED WORK
      4. EXPERIMENTAL DESIGN
      5. EXPERIMENTAL CONCLUSIONS
      6. CONCLUSION
      7. APPENDIX A: DERIVATION OF PRIORS
      8. APPENDIX B: BAYESIAN FORMULA AND DERIVATION
    6. Chapter 17: Distributed Multi-Agent Systems for a Collective Construction Task based on Virtual Swarm Intelligence
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED WORK
      4. PROBLEM STATEMENT
      5. DECENTRALIZED VIRTUAL PHEROMONE-TRAIL (DVP)-BASED COMMUNICATION MECHANISM
      6. DISTRIBUTED COORDINATION
      7. CONCLUSION
  9. Compilation of References
  10. About the Contributors
  11. Index