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Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance

Book Description

Optimization techniques have developed into a significant area concerning industrial, economics, business, and financial systems. With the development of engineering and financial systems, modern optimization has played an important role in service-centered operations and as such has attracted more attention to this field. Meta-heuristic hybrid optimization is a newly development mathematical framework based optimization technique. Designed by logicians, engineers, analysts, and many more, this technique aims to study the complexity of algorithms and problems. Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance explores the emerging study of meta-heuristics optimization algorithms and methods and their role in innovated real world practical applications. This book is a collection of research on the areas of meta-heuristics optimization algorithms in engineering, business, economics, and finance and aims to be a comprehensive reference for decision makers, managers, engineers, researchers, scientists, financiers, and economists as well as industrialists.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Editorial Advisory Board and List of Reviewers
    1. Editorial Advisory Board
    2. List of Reviewers
  5. Foreword
  6. Preface
  7. Acknowledgment
  8. Chapter 1: An Improved Particle Swarm Optimization for Optimal Power Flow
    1. ABSTRACT
    2. INTRODUCTION
    3. IMPROVED PARTICLE SWARM OPTIMIZATION FOR OPTIMAL POWER FLOW
    4. FUTURE RESEARCH DIRECTIONS
    5. CONCLUSION
    6. APPENDIX
  9. Chapter 2: The Use of Soft Computing for Optimization in Business, Economics, and Finance
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. FUZZY LOGIC
    4. 3. NEURAL NETWORKS
    5. 4. GENETIC ALGORITHMS
    6. 5 THEORY OF CHAOS
    7. FUTURE RESEARCH DIRECTIONS
    8. CONCLUSION
  10. Chapter 3: Hybrid Linear Search, Genetic Algorithms, and Simulated Annealing for Fuzzy Non-Linear Industrial Production Planning Problems
    1. ABSTRACT
    2. INTRODUCTION
    3. PROBLEM STATEMENT
    4. RESULTS AND DISCUSSION
    5. FUTURE RESEARCH DIRECTIONS
    6. CONCLUSION
  11. Chapter 4: Metaheuristic Algorithms for Supply Chain Management Problems
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND ON SUPPLY CHAIN
    4. BACKGROUND ON METAHEURISTIC ALGORITHMS
    5. SUPPLY CHAIN MANAGEMENT
    6. FUTURE RESEARCH
    7. CONCLUSION
  12. Chapter 5: Instance-Specific Parameter Tuning for Meta-Heuristics
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. PARAMETER SETTING STRATEGIES
    4. 3. INSTANCE-SPECIFIC PARAMETER TUNING
    5. 4. CASE STUDY: TSP AND GUIDED LOCAL SEARCH
    6. 5. EXPERT-BASED IPTS DESIGN USING STATISTICAL ANALYSIS
    7. 6. AUTOMATED IPTS DESIGN USING FUZZY CLUSTERING
    8. 7. DISCUSSION
    9. 8. CONCLUSION AND FUTURE RESEARCH
  13. Chapter 6: Investigating of Hybrid Meta-Heuristics to Solve the Large-Scale Multi-Source Weber Problems and Performance Measuring of them with Statistical Tests
    1. ABSTRACT
    2. INTRODUCTION
    3. LITERATURE REVIEW
    4. INVESTIGATED ALGORITHMS
    5. INVESTIGATED HYBRID ALGORITHMS
    6. SOLUTIONS AND RECOMMENDATIONS
    7. DISCUSSION
    8. FUTURE RESEARCH DIRECTIONS
    9. CONCLUSION
  14. Chapter 7: Analysing the Returns-Earnings Relationship
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. FUTURE RESEARCH DIRECTIONS
    5. CONCLUSION
  15. Chapter 8: Support Vector Machine Based Mobile Robot Motion Control and Obstacle Avoidance
    1. ABSTRACT
    2. INTRODUCTION
    3. THE FUNCTIONS OF SVM
    4. MODEL OF TWMR AND PATH PLANNING
    5. CONTROLLER DESIGN IN STATIC ENVIRONMENT
    6. CONTROLLER DESIGN IN DYNAMIC ENVIRONMENT
    7. CONCLUSION
  16. Chapter 9: A Hybrid Meta-Heuristic to Solve a Multi-Criteria HFS Problem
    1. ABSTRACT
    2. INTRODUCTION
    3. PROBLEM FORMULATION
    4. MULTI-CRITERIA OPTIMIZATION
    5. DESCRIPTION OF THE PROPOSED APPROACH
    6. EXPERIMENTAL DESIGN AND COMPUTATIONAL RESULTS
    7. CONCLUSION
    8. APPENDIX
  17. Chapter 10: Pure and Hybrid Metaheuristics for the Response Time Variability Problem
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. THE RESPONSE TIME VARIABILITY PROBLEM (RTVP)
    4. 3. PURE METAHEURISTICS FOR THE RESPONSE TIME VARIABILITY PROBLEM
    5. 4. HYBRID METAHEURISTICS FOR THE RESPONSE TIME VARIABILITY PROBLEM
    6. 5. CONCLUSION AND FUTURE RESEARCH DIRECTIONS
  18. Chapter 11: Hybrid Metaheuristics Algorithms for Inventory Management Problems
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND ON HYBRID META HEURISTICS ALGORITHMS
    4. INVENTORY MANAGEMENT
    5. EOQ PROBLEM
    6. NEWSBOY PROBLEM
    7. STOCHATIC REVIEW PROBLEM
    8. FUTURE RESEARCH
    9. CONCLUSION
    10. APPENDIX A: DEFINITIONS IN FUZZY AND ROUGH ENVIRONMENTS
  19. Chapter 12: ANN-Based Self-Tuning Frequency Control Design for an Isolated Microgrid
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. MICROGRID CONCEPT AND STRUCTURE
    5. FREQUENCY CONTROL
    6. TEST SYSTEM
    7. ANN-BASED SELF-TUNING FREQUENCY CONTROL
    8. SIMULATION RESULTS
    9. FUTURE RESEARCH DIRECTIONS
    10. CONCLUSION
  20. Chapter 13: Soccer Game Optimization
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. THE FUNDAMENTALS OF METAHEURISTICS
    5. THE SOCCER GAME OPTIMIZATION
    6. APPLICATION EXAMPLES
    7. DISCUSSION
    8. FUTURE RESEARCH DIRECTIONS
    9. CONCLUSION
  21. Chapter 14: Two Stage Capacitated Facility Location Problem
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. PROBLEM FORMULATIONS
    4. 3. LAGRANGIAN BOUNDS AND FEASIBLE LAGRANGIAN BASED SOLUTIONS
    5. 4. COMPUTATIONAL RESULTS
    6. 5. DISCUSSION
    7. 6. FINAL REMARKS AND CONCLUSION
  22. Chapter 15: Generators Maintenance Scheduling Using Music-Inspired Harmony Search Algorithm
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. BACKGROUND
    4. 3. GMS MODEL FORMULATION
    5. 4. MIHS ALGORITHM
    6. 5. RESULTS AND DISCUSSION
    7. 6. FUTURE RESEARCH DIRECTIONS
    8. 7. CONCLUSION
    9. APPENDIX
  23. Chapter 16: Usage of Metaheuristics in Engineering
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. METAHEURISTIC TECHNIQUES IN ENGINEERING
    4. 3. FINDINGS AND DISCUSSION
    5. 4. CONCLUSION
    6. 5. FUTURE RESEARCH DIRECTIONS
  24. Chapter 17: Online Clustering and Outlier Detection
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. BACKGROUND
    4. 3. THE PROPOSED METHOD
    5. 4. FUTURE RESEARCH DIRECTIONS
    6. 5. CONCLUSION
  25. Chapter 18: Optimal Ordering of Activities of New Product Development Projects with Time and Cost Considerations
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. PROPOSED SOLUTION ALGORITHM
    5. EXPERIMENTAL RESULTS
    6. FUTURE RESEARCH DIRECTIONS
    7. CONCLUSION
  26. Chapter 19: Application of Meta-Heuristic Optimization Algorithms in Electric Power Systems
    1. ABSTRACT
    2. INTRODUCTION
    3. 1. GENETIC ALGORITHMS (GA)
    4. 2. SIMULATED ANNEALING METHOD (SA)
    5. 3. ANT COLONY OPTIMIZATION
    6. 4. ARTIFICIAL NEURAL NETWORK (ANN)
    7. 5. COMBINATION OF METAHEURISTIC METHODS WITH FUZZY LOGIC AND ANN
    8. 6. CONCLUSION AND FUTURE RESEARCH DIRECTIONS
  27. Chapter 20: A Gravitational Search Algorithm Approach for Optimizing Closed-Loop Logistics Network
    1. ABSTRACT
    2. 1. AN INTRODUCTION TO SUPPLY CHAIN MANAGEMENT
    3. 2. BASIC CONCEPT AND RELATED WORK
    4. 3. CLOSED-LOOP SUPPLY CHAIN MODELING: AUTOMOBILE ALTERNATOR RECYCLING AS A CASE STUDY
    5. 4. MODEL OPTIMIZATION: GRAVITATIONAL SEARCH ALGORITHM
    6. 5. EXPERIMENTAL RESULTS
    7. 6. CONCLUSION AND FUTURE RESEARCH DIRECTIONS
  28. Compilation of References
  29. About the Contributors