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Handbook of Research on Swarm Intelligence in Engineering

Book Description

Swarm Intelligence has recently emerged as a next-generation methodology belonging to the class of evolutionary computing. As a result, scientists have been able to explain and understand real-life processes and practices that previously remained unexplored. The Handbook of Research on Swarm Intelligence in Engineering presents the latest research being conducted on diverse topics in intelligence technologies such as Swarm Intelligence, Machine Intelligence, Optical Engineering, and Signal Processing with the goal of advancing knowledge and applications in this rapidly evolving field. The enriched interdisciplinary contents of this book will be a subject of interest to the widest forum of faculties, existing research communities, and new research aspirants from a multitude of disciplines and trades.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Book Series
    1. Mission
    2. Coverage
  5. Dedication
  6. Editorial Advisory Board
  7. Preface
  8. Section 1: Theoretical Foundations
    1. Chapter 1: Quantum Behaved Swarm Intelligent Techniques for Image Analysis
      1. ABSTRACT
      2. INTRODUCTION
      3. THRESHOLDING EVALUATION METRICS
      4. SWARMS
      5. PRINCIPLES OF SWARM INTELLIGENCE
      6. OVERVIEW OF META-HEURISTIC ALGORITHMS
      7. QUANTUM COMPUTING FUNDAMENTALS
      8. QUANTUM BASED ALGORITHMS
      9. CONCLUSION
      10. REFERENCES
      11. ADDITIONAL READING
      12. KEY TERMS AND DEFINITIONS
    2. Chapter 2: An Uncertainty-Based Model for Optimized Multi-Label Classification
      1. ABSTRACT
      2. INTRODUCTION
      3. STANDARD PSO ALGORITHM
      4. PARAMETER SELECTION IN PSO
      5. ADAPTIVE PSO
      6. PSEUDO CODE OF LADDER PSO
      7. DISTRIBUTED PSO
      8. FEATURE SELECTION
      9. CLASSIFICATION
      10. CLUSTERING
      11. MULTI LABEL CLASSIFICATION
      12. A DYNAMIC CLUSTERING ALGORITHM FOR MULTI LABEL CLASSIFICATION
      13. CONCLUSION AND FUTURE WORK
      14. REFERENCES
      15. KEY TERMS AND DEFINITIONS
    3. Chapter 3: Swarm Intelligence in Solving Bio-Inspired Computing Problems
      1. ABSTRACT
      2. INTRODUCTION
      3. INFORMATION SYSTEM
      4. FOUNDATIONS OF ROUGH COMPUTING
      5. THE CONCEPT OF CORE AND REDUCT
      6. OVERVIEW OF SWARM INTELLIGENCE MODELS
      7. SWARM INTELLIGENCE IN MICROARRAY CLASSIFICATION
      8. SWARM INTELLIGENCE IN GENE EXPRESSION
      9. SWARM INTELLIGENCE IN HEALTHCARE
      10. SWARM INTELLIGENCE IN MEDICAL DECISION SUPPORT SYSTEM
      11. FUTURE RESEARCH DIRECTIONS
      12. CONCLUSION
      13. REFERENCES
      14. KEY TERMS AND DEFINITIONS
    4. Chapter 4: Studies of Computational Intelligence Based on the Behaviour of Cockroaches
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. BACKGROUND
      4. 3. APPLICATIONS OF COCKROACH SWARM INTELLIGENCE
      5. 4. THEORETICAL STUDY COCKROACH SWARM OPTIMIZATION AND AN EXPERIMENTAL RESULTS OF INTEGRATION OF ROBOT TO COCKROACH FOR COLLECTIVE DECISION MAKING
      6. 5. PROPOSED ALGORITHM FOR TRAVERSING SHORTEST DISTANCE CITY WAREHOUSE USING COCKROACH SWARM OPTIMIZATION
      7. 6. DISCUSSION
      8. 7. LIMITATIONS OF COCKROACH SWARM OPTIMIZATION ALGORITHM FOR CITY WAREHOUSE TRAVERSING PROBLEM
      9. 8. FUTURE PROSPECT
      10. 9. COMPARATIVE STATISTICAL RESULTS OF THE PROGRESS OF THE WORK ON COCKROACH
      11. 10. CONCLUSION
      12. REFERENCES
      13. ADDITIONAL READING
      14. KEY TERMS AND DEFINITIONS
      15. APPENDIX
    5. Chapter 5: Swarm Intelligence for Biometric Feature Optimization
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. BIOMETRIC SYSTEM
      4. 3. FACE AS BIOMETRIC SYSTEM
      5. 4. FEATURE-BASED METHODS
      6. 5. FEATURE SELECTION (FS)
      7. 6. PROPOSED FACE RECOGNITION METHODOLOGY USING SWARM INTELLIGENCE
      8. 7. CONCLUSION AND FUTURE DIRECTION
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
    6. Chapter 6: Minimax Probability Machine
      1. ABSTRACT
      2. INTRODUCTION
      3. DETAILS OF MPM
      4. RESULTS AND DISCUSSION
      5. CONCLUSION
      6. REFERENCES
      7. ADDITIONAL READING
      8. KEY TERMS AND DEFINITIONS
    7. Chapter 7: Swarm-Based Mean-Variance Mapping Optimization (MVMOS) for Solving Non-Convex Economic Dispatch Problems
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. PROBLEM FORMULATION
      5. MEAN-VARIANCE MAPPING OPTIMIZATION
      6. IMPLEMENTATION OF MVMO TO NONCONVEX ED PROBLEMS
      7. NUMERICAL RESULTS
      8. DISCUSSION
      9. FUTURE RESEARCH DIRECTIONS
      10. CONCLUSION
      11. ACKNOWLEDGMENT
      12. REFERENCES
      13. ADDITIONAL READING
      14. APPENDIX
    8. Chapter 8: Advanced Strategy for Droplet Routing in Digital Microfluidic Biochips Using ACO
      1. ABSTRACT
      2. INTRODUCTION
      3. LITERATURE SURVEY
      4. PROBLEM FORMULATION
      5. DESIGN CONSTRAINTS FOR DROPLET ROUTING
      6. PROPOSED METHOD
      7. PHEROMONE EVAPORATION RATE AND ACO CONVERGENCE ANALYSIS
      8. EXPERIMENTAL RESULTS
      9. FUTURE RESEARCH DIRECTIONS
      10. CONCLUSION
      11. REFERENCES
      12. ADDITIONAL READING
      13. KEY TERMS AND DEFINITIONS
  9. Section 2: Applications
    1. Chapter 9: Quantum Inspired Swarm Optimization for Multi-Level Image Segmentation Using BDSONN Architecture
      1. ABSTRACT
      2. INTRODUCTION
      3. IMAGE SEGMENTATION
      4. PRINCIPLES OF MULTILEVEL IMAGE SEGMENTATION USING BDSONN ARCHITECTURE
      5. MUSIG AND OPTIMUSIG ACTIVATION FUNCTION
      6. PARTICLE SWARM OPTIMIZATION
      7. ANT COLONY OPTIMIZATION
      8. FUNDAMENTALS OF QUANTUM COMPUTING
      9. QUANTUM INSPIRED PARTICLE SWARM OPTIMIZATION
      10. QUANTUM INSPIRED ANT COLONY OPTIMIZATION
      11. EXPERIMENTAL RESULTS
      12. CONCLUSION
      13. REFERENCES
      14. ADDITIONAL READING
      15. KEY TERMS AND DEFINITIONS
    2. Chapter 10: Image Enhancement Techniques Using Particle Swarm Optimization Technique
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. APPLICATIONS OF PARTICLE SWARM OPTIMIZATION (PSO) TECHNIQUE IN IMAGE ENHANCEMENT PROCESSES
      5. OTHER APPLICATIONS OF PSO IN IMAGE PROCESSING
      6. CONCLUSION
      7. REFERENCES
      8. KEY TERMS AND DEFINITIONS
    3. Chapter 11: A Self-Organized Software Deployment Architecture for a Swarm Intelligent MANET
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. PROPOSED SCHEME
      5. EXAMPLE
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
      8. DEDICATION
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
    4. Chapter 12: Swarm Intelligence-Based Optimization for PHEV Charging Stations
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MAIN FOCUS OF THE CHAPTER
      5. PROPOSED METHODS
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
      8. ACKNOWLEDGMENT
      9. REFERENCES
      10. ADDITIONAL READING
      11. KEY TERMS AND DEFINITIONS
      12. APPENDIX: NOMENCLATURE
    5. Chapter 13: Particle Swarm Optimization Algorithm as a Tool for Profiling from Predictive Data Mining Models
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. IMPORTANCE OF CUSTOMER PROFILING
      5. PARTICLE SWARM OPTIMISATION ALGORITHM AND PROFILING FROM PREDICTIVE MODELS
      6. FUTURE RESEARCH DIRECTIONS
      7. DISCUSSION
      8. CONCLUSION
      9. REFERENCES
      10. ADDITIONAL READING
      11. KEY TERMS AND DEFINITIONS
    6. Chapter 14: Remote Sensing Image Classification Using Fuzzy-PSO Hybrid Approach
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. RELATED WORKS
      5. MEMBERSHIP BASED FUZZY PSO ALGORITHM
      6. APPLICATION OF FPSO ALGORITHM IN PIXEL CLASSIFICATION OF REMOTE SENSING IMAGERY
      7. PERFORMANCE ANALYSIS
      8. TEST FOR STATISTICAL SIGNIFICANCE
      9. DISCUSSION
      10. FUTURE RESEARCH WORKS
      11. CONCLUSION
      12. REFERENCES
      13. ADDITIONAL READING
      14. KEY TERMS AND DEFINITIONS
    7. Chapter 15: Particle Swarm Optimization Method to Design a Linear Tubular Switched Reluctance Generator
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. OPTIMIZATION PROBLEM
      5. DESCRIPTION OF THE STRUCTURAL MODELS
      6. RESULTS
      7. CONCLUSION
      8. ACKNOWLEDGMENT
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
    8. Chapter 16: Derivation and Simulation of an Efficient QoS Scheme in MANET through Optimised Messaging Based on ABCO Using QualNet
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. GENERALITIES AND CHALLENGES IN AD HOC NETWORK
      4. 3. QUALITY OF SERVICES IN MANET
      5. 4. SWARM INTELLIGENCE
      6. 5. ALGORITHM PROLOGUE AND OUR ALGORITHM
      7. 6. SIMULATION RESULTS AND ANALYSIS
      8. 7. CONCLUSION
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
    9. Chapter 17: A Uniformly Distributed Mobile Sensor Nodes Deployment Strategy Using Swarm Intelligence
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. APPLICATIONS OF WSN
      4. 3. CHARACTERISTICS OF WSN
      5. 4. RESEARCH CHALLENGES IN WSN
      6. 5. PERFORMANCE INDEX OF NODE DEPLOYMENT
      7. 6. PRINCIPLE OF SWARM INTELLIGENCE
      8. 7. DEPLOYMENT PROBLEMS
      9. 8. OUR PROPOSAL
      10. 9. SIMULATION ENVIRONMENT
      11. 10. RESULT AND DISCUSSION
      12. 11. CONCLUSION AND CHALLENGES AHEAD
      13. REFERENCES
      14. KEY TERMS AND DEFINITIONS
    10. Chapter 18: Applications of Particle Swarm Optimization in Composite Power System Reliability Evaluation
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. BACKGROUND
      4. 3. NETWORK MODELLING USING LINEAR PROGRAMMING AND DC POWER FLOW
      5. 4. STATE SPACE REDUCTION TECHNIQUE
      6. 5. REDUCTION IN COMPUTATIONAL TIME
      7. 6. THE COMPLEMENTARY CONCEPT
      8. 7. DYNAMICALLY DIRECTED DISCRETE PARTICLE SWARM OPTIMISATION SEARCH METHOD
      9. 8. COMPARISON BETWEEN THE PROPOSED METHOD AND THE EXISTING METHODS
      10. 9. CALCULATION OF THE RELIABILITY INDICES
      11. 10. SENSITIVITY ANALYSIS
      12. 11. SOLUTION ALGORITHM
      13. 12. APPLICATION
      14. 13. CONCLUSION
      15. REFERENCES
      16. KEY TERMS AND DEFINITIONS
    11. Chapter 19: Ambiguity Reduction through Optimal Set of Region Selection Using GA and BFO for Handwritten Bangla Character Recognition
      1. ABSTRACT
      2. INTRODUCTION
      3. 1. GENETIC ALGORITHMS
      4. CONCLUSION
      5. REFERENCES
      6. KEY TERMS AND DEFINITIONS
    12. Chapter 20: Particle Swarm Optimization-Based Session Key Generation for Wireless Communication (PSOSKG)
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. BACKGROUND
      4. 3. THE OBJECTIVE
      5. 4. THE TECHNIQUE
      6. 5. PSO BASED SESSION KEY GENERATION ALGORITHM
      7. 6. ENCRYPTION ALGORITHM
      8. 7. DECRYPTION ALGORITHM
      9. 8. IMPLEMENTATION
      10. 9. RESULTS AND ANALYSIS
      11. 10. CONCLUSION
      12. REFERENCES
      13. KEY TERMS AND DEFINITIONS
  10. Compilation of References
  11. About the Contributors