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Problem Solving and Uncertainty Modeling through Optimization and Soft Computing Applications

Book Description

Optimization techniques have developed into a modern-day solution for real-world problems in various industries. As a way to improve performance and handle issues of uncertainty, optimization research becomes a topic of special interest across disciplines. Problem Solving and Uncertainty Modeling through Optimization and Soft Computing Applications presents the latest research trends and developments in the area of applied optimization methodologies and soft computing techniques for solving complex problems. Taking a multi-disciplinary approach, this critical publication is an essential reference source for engineers, managers, researchers, and post-graduate students.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Book Series
    1. Mission
    2. Coverage
  5. Editorial Advisory Board and List of Reviewers
    1. Editorial Advisory Board
    2. List of Reviewers
  6. Preface
  7. Section 1: Optimization Techniques for Problem Solving
    1. Chapter 1: A Cultivated Variant of Differential Evolution Algorithm for Global Optimization
      1. ABSTRACT
      2. MAIN FOCUS OF THE CHAPTER
      3. INTRODUCTION
      4. DIFFERENTIAL EVOLUTION ALGORITHM
      5. PROPOSED ALGORITHM
      6. APPLICATION OF THE PROPOSED ALGORITHM
      7. EXPERIMENTAL SET-UP
      8. NUMERICAL RESULTS AND COMPARISONS
      9. FUTURE RESEARCH DIRECTIONS
      10. CONCLUSION
      11. REFERENCES
      12. ENDNOTES
      13. APPENDIX
    2. Chapter 2: A Novel Mutation Strategy for Differential Evolution
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. DIFFERENTIAL EVOLUTION
      4. 3. EXPERIMENTAL SETTING
      5. 4. RESULTS AND DISCUSSION
      6. 5. PERFORMANCE CURVES OF DIFFERENT FUNCTIONS
      7. 6. CONCLUSION
      8. ACKNOWLEDGEMENT
      9. REFERENCES
    3. Chapter 3: Behavioral Study of Drosophila Fruit Fly and Its Modeling for Soft Computing Application
      1. ABSTRACT
      2. INTRODUCTION
      3. DROSOPHILA MELANOGASTER
      4. LIGAND
      5. G-PROTEIN-COUPLED-RECEPTOR (GPCR)
      6. TOURNAMENT SELECTION
      7. REDUNDANT SEARCH (RS)
      8. MODIFIED QUADRATIC APPROXIMATION (mQA
      9. DROSOPHILA FOOD SEARCH OPTIMIZATION (DFO) ALGORITHM
      10. NUMERICAL RESULTS AND DISCUSSION (UNCONSTRAINT PROBLEMS)
      11. UNCONSTRAINED REAL LIFE PROBLEMS
      12. CONSTRAINT HANDLING TECHNIQUE
      13. DROSOPHILA FOOD SEARCH OPTIMIZATION FOR CONSTRAINED PROBLEMS
      14. NUMERICAL RESULTS AND DISCUSSION (CONSTRAINT PROBLEMS)
      15. CONSTRAINED REAL LIFE PROBLEMS
      16. NUMERICAL RESULTS AND DISCUSSION
      17. CONCLUSION
      18. REFERENCES
      19. APPENDIX 1
      20. APPENDIX 2
    4. Chapter 4: Hookes-Jeeves-Based Variant of Memetic Algorithm
      1. ABSTRACT
      2. INTRODUCTION
      3. MATHEMATICAL FORM OF OPTIMIZATION PROBLEMS
      4. LOCAL AND GLOBAL OPTIMAL SOLUTIONS
      5. METHODS FOR GLOBAL OPTIMIZATION
      6. GENETIC ALGORITHMS
      7. HYBRIDIZED GENETIC ALGORITHMS
      8. LOCAL SEARCH HOOKE-JEEVES METHOD
      9. METHODOLOGY OF THE PROPOSED APPROACH
      10. SALIENT FEATURES OF MA
      11. CONCLUSION
      12. REFERENCES
      13. ADDITIONAL READING
      14. APPENDIX
    5. Chapter 5: Simulation Tool for Transportation Problem
      1. ABSTRACT
      2. INTRODUCTION
      3. CONCLUSION
      4. REFERENCES
  8. Section 2: Applications of Optimization (Non-Soft Computing) Techniques
    1. Chapter 6: Comparative Study of GRA and MOORA Methods
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. METHODOLOGY OF GRA AND MOORA METHOD
      4. 3. WEIGHT DETERMINATION METHODS
      5. 4. A CASE STUDY: RANKING AND SELECTION OF TFO MACHINE
      6. 5. CONCLUSION
      7. REFERENCES
    2. Chapter 7: Optimization of Dairy Feeding Models with C-SOMGA
      1. ABSTRACT
      2. INTRODUCTION
      3. MATERIAL AND METHODS
      4. TOURNAMENT SELECTION IN CONSTRAINED OPTIMIZATION
      5. METHODOLOGY OF C-SOMGA
      6. INPUT DATA FOR DETERMINATION OF FEED BLEND
      7. DISCUSSION
      8. CONCLUSION
      9. REFERENCES
    3. Chapter 8: Modeling Stock Prices Using Monte-Carlo Simulation and Excel
      1. ABSTRACT
      2. THE NATURE OF SIMULATION
      3. STEPS IN SIMULATION STUDY
      4. MONTE-CARLO AND SPREADSHEET SIMULATION
      5. APPLICATION ONE: MONTE-CARLO AND INTEGRATION
      6. CONCLUSION
      7. REFERENCES
    4. Chapter 9: MCOQR (Misuse Case-Oriented Quality Requirements) Metrics Framework
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED TERMINOLOGIES
      4. RELATED WORK
      5. LIMITATION OF THE STUDY
      6. PROBLEM DEFINITION
      7. PROPOSED MCOQR (MISUSE CASE ORIENTED QUALITY REQUIREMENTS) METRICS FRAMEWORK
      8. WORKING OF PROPOSED MCOQR (MISUSE CASE ORIENTED QUALITY REQUIREMENTS) METRICS FRAMEWORK
      9. OBJECTIVE OF PROPOSED MCOQR (MISUSE CASE ORIENTED QUALITY REQUIREMENTS) METRICS FRAMEWORK
      10. VALIDATION AND EXPERIMENTAL RESULTS
      11. CONCLUSION AND FUTURE WORK
      12. REFERENCES
  9. Section 3: Applications of Soft Computing Techniques
    1. Chapter 10: Performance Analysis of DE over K-Means Proposed Model of Soft Computing
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. RELATED WORK
      4. 3. SOFT COMPUTING TECHNIQUES
      5. 4. GENETIC ALGORITHM
      6. 5. DIFFERENTIAL EVOLUTION ALGORITHM
      7. 6. K-MEANS CLUSTERING
      8. 7. PROPOSED MODEL
      9. 8. DATA SETS AND EXPERIMENTAL RESULT
      10. 9. CONCLUSION
      11. REFERENCES
    2. Chapter 11: Application of Shuffled Frog Leaping Algorithm in Software Project Scheduling
      1. ABSTRACT
      2. INTRODUCTION
      3. SOFTWARE PROJECT SCHEDULING (SPS) PROBLEM
      4. SHUFFLED FROG LEAPING ALGORITHM: AN OVERVIEW
      5. ACCELERATED SFLA (A-SFLA)
      6. CONSTRAINED HANDLING AND EXPERIMENTAL SETUP
      7. RESULT DISCUSSION
      8. CONCLUSION AND FUTURE WORK
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
    3. Chapter 12: Sustainable Supplier's Management Using Differential Evolution
      1. ABSTRACT
      2. INTRODUCTION
      3. SSCM IN AUTOMOTIVE INDUSTRY
      4. SUSTAINABLE SUPPLIER SELECTION
      5. METHODOLOGY
      6. MATHEMATICAL MODEL FORMULATION WITH DEA
      7. DE ALGORITHM
      8. CASES ON AUTOMOTIVE INDUSTRY
      9. CASE ON PULP AND PAPER INDUSTRY
      10. CONCLUSION AND SUMMARY
      11. REFERENCES
    4. Chapter 13: Comparison of Analytical and Heuristic Techniques for Multiobjective Optimization in Power System
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. BACKGROUND
      4. 3. MAIN FOCUS OF THE CHAPTER
      5. 4. SOLUTIONS AND RECOMMENDATIONS
      6. 5. RESULT AND CONCLUSION
      7. REFERENCES
    5. Chapter 14: Determination of Bearing Capacity of Shallow Foundation Using Soft Computing
      1. ABSTRACT
      2. INTRODUCTION
      3. GAUSSIAN PROCESSES REGRESSION
      4. EXTREME LEARNING MACHINE
      5. MINIMAX PROBABILITY MACHINE REGRESSION
      6. DEVELOPMENT OF MODEL
      7. RESULTS AND DISCUSSIONS
      8. CONCLUSION
      9. REFERENCES
      10. ADDITIONAL READING
      11. KEY WORDS AND DEFINITIONS
    6. Chapter 15: Optimizing Website Content to Improve Correctness of the Website Design
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED WORK
      4. METHODOLOGY
      5. EVALUATION
      6. CONCLUSION
      7. REFERENCES
      8. KEY TERMS AND DEFINITIONS
    7. Chapter 16: Wavelet Transform-Based Soft Computational Techniques and Applications in Medical Imaging
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. WAVELET TRANSFORM
      4. 3. ARTIFICIAL NEURAL NETWORK
      5. 4. WAVELET NEURAL NETWORK
      6. 5. APPLICATIONS IN MEDICAL IMAGING
      7. 6. CONCLUSION
      8. REFERENCES
  10. Compilation of References
  11. About the Contributors