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Handbook of Research on Soft Computing and Nature-Inspired Algorithms

Book Description

Soft computing and nature-inspired computing both play a significant role in developing a better understanding to machine learning. When studied together, they can offer new perspectives on the learning process of machines. The Handbook of Research on Soft Computing and Nature-Inspired Algorithms is an essential source for the latest scholarly research on applications of nature-inspired computing and soft computational systems. Featuring comprehensive coverage on a range of topics and perspectives such as swarm intelligence, speech recognition, and electromagnetic problem solving, this publication is ideally designed for students, researchers, scholars, professionals, and practitioners seeking current research on the advanced workings of intelligence in computing systems.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Book Series
    1. Mission
    2. Coverage
  5. Dedication
  6. Foreword
  7. Preface
  8. Chapter 1: Application of Natured-Inspired Algorithms for the Solution of Complex Electromagnetic Problems
    1. ABSTRACT
    2. INTRODUCTION
    3. 2. DESCRIPTION OF NIO ALGORITHMS
    4. 3. NUMERICAL ASSESSMENT
    5. CONCLUSION
    6. REFERENCES
  9. Chapter 2: A Comprehensive Literature Review on Nature-Inspired Soft Computing and Algorithms
    1. ABSTRACT
    2. INTRODUCTION
    3. SOFT COMPUTING
    4. NATURE-INSPIRED SOFT COMPUTING
    5. FUTURE OF NATURE-INSPIRED SOFT COMPUTING
    6. CONCLUSION
    7. REFERENCES
  10. Chapter 3: Swarm Intelligence for Electromagnetic Problem Solving
    1. ABSTRACT
    2. INTRODUCTION
    3. MAIN FOCUS OF THE CHAPTER
    4. CLASSIC PSO ALGORITHM – GENERAL FRAMEWORK
    5. QUANTUM-BEHAVED PSO ALGORITHM – GENERAL FRAMEWORK
    6. BENCHMARK TESTS FOR THE EWQPSO ALGORITHM
    7. EWQPSO FOR SUPERSHAPED LENS ANTENNA SYNTHESIS
    8. FDTD/EWQPSO METHOD FOR ELECTROMAGNETIC CHARACTERIZATION OF DISPERSIVE LAYERED SYSTEMS
    9. CONCLUSION
    10. REFERENCES
    11. ADDITIONAL READING
  11. Chapter 4: Parameter Settings in Particle Swarm Optimization
    1. ABSTRACT
    2. INTRODUCTION
    3. MAIN FOCUS OF THE CHAPTER
    4. PARTICLE SWARM OPTIMIZATION
    5. PARAMETER TUNING AND CONTROL
    6. THEORETICAL ANALYSIS OF PSO
    7. STATES OF PSO
    8. PARAMETER TUNING AND CONTROL FOR PSO
    9. PSO VARIANT ANALYSIS
    10. APPLICATIONS OF PSO
    11. FUTURE RESEARCH DIRECTIONS
    12. CONCLUSION
    13. REFERENCES
    14. KEY TERMS AND DEFINITIONS
  12. Chapter 5: A Survey of Computational Intelligence Algorithms and Their Applications
    1. ABSTRACT
    2. INTRODUCTION
    3. EVOLUTIONARY ALGORITHMS (EA)
    4. GENETIC ALGORITHMS (GAS)
    5. GENETIC PROGRAMMING (GP)
    6. EVOLUTIONARY STRATEGY (ES)
    7. DIFFERENTIAL EVOLUTION (DE)
    8. PADDY FIELD ALGORITHM (PFA)
    9. ANALYTIC COMPARISON BETWEEN EVOLUTIONARY ALGORITHMS
    10. SWARM INTELLIGENCE ALGORITHM (SIAS)
    11. PARTICLES SWARM OPTIMIZATION (PSO) ALGORITHM
    12. ANT COLONY OPTIMIZATION ALGORITHM
    13. BACTERIAL FORAGING OPTIMIZATION (BFO)
    14. WOLF COLONY ALGORITHM (WCA)
    15. WOLF PACKS SEARCH (WPS)
    16. BAT ALGORITHM (BA)
    17. COCKROACHES COLONY ALGORITHM (CCA)
    18. MOSQUITO OPTIMIZATION ALGORITHM (MPA)
    19. WASP SWARM OPTIMIZATION (WSO):\
    20. SOCIAL SPIDERS’ ALGORITHM (SSA)
    21. THE CUCKOO SEARCH ALGORITHM (CSA)
    22. FIREWORKS ALGORITHM (FWA)
    23. ANALYTIC COMPARISON BETWEEN SWARM INTELLIGENCE ALGORITHMS
    24. CONCLUSION
    25. REFERENCES
  13. Chapter 6: Optimization of Process Parameters Using Soft Computing Techniques
    1. ABSTRACT
    2. INTRODUCTION
    3. LITERATURE REVIEW
    4. DESIGN OF EXPERIMENTS
    5. TAGUCHI ANALYSIS
    6. GREY RELATIONAL ANALYSIS
    7. WEIGHTED PRINCIPAL COMPONENT ANALYSIS (WPCA)
    8. EXPERIMENTATION
    9. RESULT AND DISCUSSION
    10. CONCLUSION
    11. REFERENCES
  14. Chapter 7: Augmented Lagrange Hopfield Network for Combined Economic and Emission Dispatch with Fuel Constraint
    1. ABSTRACT
    2. NOMENCLATURE
    3. INTRODUCTION
    4. ALHN FOR COMBINED ECONOMIC AND EMISSION DISPATCH WITH FUEL CONSTRAINT
    5. CONCLUSION
    6. REFERENCES
    7. APPENDIX
  15. Chapter 8: Speaker Recognition With Normal and Telephonic Assamese Speech Using I-Vector and Learning-Based Classifier
    1. ABSTRACT
    2. INTRODUCTION
    3. THEORETICAL CONSIDERATIONS
    4. LITERATURE SURVEY OF VARIOUS WORKS DONE RELATED TO OUR TOPICS OF DISCUSSION
    5. SYSTEM MODEL AND SPEECH DATABASE
    6. EXPERIMENTAL RESULTS AND DISCUSSIONS
    7. CONCLUSION
    8. REFERENCES
  16. Chapter 9: A New SVM Method for Recognizing Polarity of Sentiments in Twitter
    1. ABSTRACT
    2. INTRODUCTION
    3. RELATED WORK
    4. PROPOSED METHOD
    5. EXPERIMENTS AND OBSERVATIONS
    6. CONCLUSION
    7. REFERENCES
  17. Chapter 10: Automatic Generation Control of Multi-Area Interconnected Power Systems Using Hybrid Evolutionary Algorithm
    1. ABSTRACT
    2. INTRODUCTION
    3. EVOLUTIONARY ALGORITHMS
    4. SIMULATED ANNEALING
    5. POWER SYSTEM MODELS UNDER INVSTIGATION
    6. OPTIMAL CONTROL STRATEGY
    7. OPTIMAL HYBRID EA DESIGN TECHNIQUE
    8. FLOW CHART OF HYBRID GASA TECHNIQUE
    9. RESULTS DISCUSSION
    10. CONCLUSION
    11. REFERENCES
    12. APPENDIX
  18. Chapter 11: Mathematical Optimization by Using Particle Swarm Optimization, Genetic Algorithm, and Differential Evolution and Its Similarities
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. LITERATURE REVIEW
    4. 3. PARTICLE SWARM OPTIMIZATION
    5. 4. GENETIC ALGORITHM
    6. 5. DIFFERENTIAL EVOLUTION
    7. 6. RESEARCH DIRECTIONS
    8. 7. CONCLUSION
    9. REFERENCES
  19. Chapter 12: GA_SVM
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. BRIEF DESCRIPTION OF ATTRIBUTE SELECTION, GA, CLASSIFICATION, AND SVM
    4. 3. PROBLEM SPECIFICATION
    5. 4. RELATED WORK
    6. 5. PROPOSED METHODOLOGY
    7. 6. RESULTS AND DISCUSSION OF PROPOSED METHODOLOGY
    8. 7. CONCLUSION AND FUTURE WORK
    9. REFERENCES
  20. Chapter 13: The Insects of Nature-Inspired Computational Intelligence
    1. ABSTRACT
    2. INTRODUCTION
    3. INSECT ORIENTED ALGORITHM MODEL
    4. SUMMARY OF INSECTS INSPIRED COMPUTATIONAL INTELLIGENCE
    5. CONCLUSION
    6. REFERENCES
  21. Chapter 14: Bio-Inspired Computational Intelligence and Its Application to Software Testing
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. BACKGROUNDS
    4. 3. SWARM INTELLIGENCE
    5. 4. APPLICATIONS OF EVOLUTIONARY ALGORITHMS IN SOFTWARE TEST OPTIMIZATION
    6. 5. TEST DATA GENERATION USING EVOLUTIONARY STRATEGIES
    7. 6. APPLICATION OF ACO, ABC TO THE SOFTWARE TEST DATA GENERATION PROBLEM
    8. 7. CONCLUSION
    9. REFERENCES
  22. Chapter 15: Quantum-Inspired Computational Intelligence for Economic Emission Dispatch Problem
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. LITERATURE REVIEW
    5. PROPOSED METHODS
    6. DISCUSSION
    7. CONCLUSION
    8. ACKNOWLEDGMENT
    9. REFERENCES
  23. Chapter 16: Intelligent Expert System to Optimize the Quartz Crystal Microbalance (QCM) Characterization Test
    1. ABSTRACT
    2. INTRODUCTION
    3. CASE OF STUDY
    4. MODEL APPROACH
    5. RESULTS
    6. CONCLUSION
    7. REFERENCES
  24. Chapter 17: Optimization Through Nature-Inspired Soft-Computing and Algorithm on ECG Process
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. PLANNING FOR EXPERIMENTATION
    4. 3. ARTIFICIAL NEURAL NETWORK
    5. 4. CUCKOO SEARCH ALGORITHM FOLLOWING LEVY FLIGHTS
    6. 5. GENETIC ALGORITHM
    7. 6. MULTI OBJECTIVE MODEL USING GREY RELATION ANALYSIS
    8. 7. CONCLUSION
    9. REFERENCES
  25. Chapter 18: An Overview of the Last Advances and Applications of Artificial Bee Colony Algorithm
    1. ABSTRACT
    2. INTRODUCTION
    3. ARTIFICIAL BEE COLONY AND VARIANTS
    4. CONCLUSION
    5. REFERENCES
  26. Chapter 19: A Survey of the Cuckoo Search and Its Applications in Real-World Optimization Problems
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. CUCKOO SEARCH
    4. 3. VARIANTS OF THE CUCKOO SEARCH
    5. 4. APPLICATIONS OF CUCKOO SEARCH
    6. 5. CONCLUSION
    7. REFERENCES
  27. Compilation of References
  28. About the Contributors