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Trends in Developing Metaheuristics, Algorithms, and Optimization Approaches

Book Description

Developments in metaheuristics continue to advance computation beyond its traditional methods. With groundwork built on multidisciplinary research findings; metaheuristics, algorithms, and optimization approaches uses memory manipulations in order to take full advantage of strategic level problem solving.Trends in Developing Metaheuristics, Algorithms, and Optimization Approaches provides insight on the latest advances and analysis of technologies in metaheuristics computing. Offering widespread coverage on topics such as genetic algorithms, differential evolution, and ant colony optimization, this book aims to be a forum researchers, practitioners, and students who wish to learn and apply metaheuristic computing. 

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. List of Reviewers
    1. ASSOCIATE EDITORS
    2. LIST OF REVIEWERS
  5. Preface
    1. ABSTRACT
    2. MOMO: MULTI-OBJECTIVE METAHEURISTIC OPTIMIZATION
    3. INTRODUCTION
    4. CLASSIC MULTI-OBJECTIVE OPTIMIZATION
    5. MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS
    6. AMP-BASED MOEA
    7. CONCLUSION
  6. Chapter 1: A Novel Particle Swarm Optimization Algorithm for Multi-Objective Combinatorial Optimization Problem
    1. ABSTRACT
    2. INTRODUCTION
    3. FUNDAMENTAL CONCEPTS OF MULTI-OBJECTIVE OPTIMIZATION
    4. BINARY MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION
    5. COMBINATORIAL MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION
    6. EXPERIMENTAL STUDY
    7. EXPERIMENTAL SETUP
    8. CONCLUSION
  7. Chapter 2: A Hybrid Simulated Annealing and Simplex Method for Fixed-Cost Capacitated Multicommodity Network Design
    1. ABSTRACT
    2. INTRODUCTION
    3. CONCLUSION
  8. Chapter 3: A Hybrid Meta-Heuristic Algorithm for Dynamic Spectrum Management in Multiuser Systems
    1. ABSTRACT
    2. INTRODUCTION
    3. SYSTEM MODEL AND OBJECTIVE
    4. A COMBINED META-HEURISTICS ALGORITHM: SIMULATED ANNEALING AND SIMPLEX NELDER-MEAD
    5. PROPOSED ALGORITHM
    6. SIMULATION RESULTS
    7. CONCLUSION
  9. Chapter 4: Discrete Artificial Bee Colony Optimization Algorithm for Financial Classification Problems
    1. ABSTRACT
    2. INTRODUCTION
    3. BEES INSPIRED OPTIMIZATION ALGORITHMS
    4. DISCRETE ARTIFICIAL BEE COLONY ALGORITHM
    5. PARAMETER SELECTION
    6. RESULTS FOR THE UCI MACHINE LEARNING REPOSITORY’S DATA
    7. FINANCIAL CLASSIFICATION PROBLEM
    8. CONCLUSION
  10. Chapter 5: Vector Evaluated and Objective Switching Approaches of Artificial Bee Colony Algorithm (ABC) for Multi-Objective Design Optimization of Composite Plate Structures
    1. ABSTRACT
    2. INTRODUCTION
    3. PROBLEM FORMULATION
    4. SIMPLY SUPPORTED RECTANGULAR PLATE
    5. LAMINATE ANALYSIS
    6. TRANSVERSE LOADING CONFIGURATIONS
    7. THE OPTIMIZATION PROCESS
    8. OBJECTIVE FUNCTIONS
    9. THE WEIGHT FUNCTION
    10. THE COST FUNCTION
    11. DESIGN VARIABLES
    12. DESIGN CONSTRAINTS: FAILURE CRITERIA
    13. MAXIMUM STRESS FAILURE CRITERION
    14. FAILURE MECHANISM BASED FAILURE CRITERION (FMBFC)
    15. TSAI-WU FAILURE CRITERION
    16. TSAI-HILL FAILURE CRITERION
    17. ARTIFICIAL BEE COLONY (ABC)
    18. VECTOR EVALUATED ARTIFICIAL BEE COLONY (VEABC) FOR WEIGHT AND COST MINIMIZATION OF THE LAMINATED COMPOSITE PLATE
    19. OBJECTIVE SWITCHING ARTIFICIAL BEE COLONY (OSABC) FOR WEIGHT AND COST MINIMIZATION OF LAMINATED COMPOSITE PLATE
    20. TRANSVERSE LINE LOAD
    21. TRANSVERSE HYDROSTATIC LOAD
    22. COMPARISON OF NATURE-INSPIRED OPTIMIZATION METHODS
    23. INTERPRETING A BOX PLOT THROUGH ANOVA TEST
    24. CONCLUSION
    25. APPENDIX
  11. Chapter 6: An Ant Colony System Algorithm for the Hybrid Flow-Shop Scheduling Problem
    1. ABSTRACT
    2. INTRODUCTION
    3. LITERATURE REVIEW
    4. PROBLEM NOTATION
    5. THE PROPOSED ANT COLONY SYSTEM ALGORITHM
    6. COMPUTATIONAL EXPERIMENTS
    7. CONCLUSION
  12. Chapter 7: Discrete Particle Swarm Optimization for the Multi-Level Lot-Sizing Problem
    1. ABSTRACT
    2. INTRODUCTION
    3. MLLP: STATE OF THE ART
    4. PARTICLE SWARM OPTIMIZATION
    5. A PSO APPROACH BASED ON COST MODIFICATION
    6. CONCLUSION
  13. Chapter 8: Adaptive Non-Uniform Particle Swarm Application to Plasmonic Design
    1. ABSTRACT
    2. INTRODUCTION
    3. THE OPTIMIZATION PROBLEM OF SPR BIOSENSORS
    4. THE ADAPTIVE NON-UNIFORM OPTIMIZATION METHOD FOR PLASMONICS
    5. CONCLUSION
  14. Chapter 9: New Evolutionary Algorithm Based on 2-Opt Local Search to Solve the Vehicle Routing Problem with Private Fleet and Common Carrier
    1. ABSTRACT
    2. INTRODUCTION
    3. PROBLEM STATEMENT: DESCRIPTION OF THE VRP WITH PRIVATE FLEET AND COMMON CARRIER
    4. MAIN PARADIGM OF THE IDEA METAHEURISTIC
    5. SOLUTION METHODOLOGY
    6. COMPUTATIONAL RESULTS
    7. CONCLUSION
    8. APPENDIX
  15. Chapter 10: Scatter Search Applied to the Vehicle Routing Problem with Simultaneous Delivery and Pickup
    1. ABSTRACT
    2. INTRODUCTION
    3. CONCLUSION
  16. Chapter 11: Parallel Scatter Search Algorithms for Exam Timetabling
    1. ABSTRACT
    2. INTRODUCTION
    3. EXAM TIMETABLING PROBLEM DESCRIPTION
    4. PARALLEL SCATTER SEARCH ALGORITHMS FOR EXAM TIMETABLING
    5. PARALLEL SCATTER SEARCH ALGORITHM
    6. EMPIRICAL RESULTS
    7. RESULTS AND DISCUSSIONS
  17. Chapter 12: Pseudo-Cut Strategies for Global Optimization
    1. ABSTRACT
    2. INTRODUCTION
    3. PSEUDO-CUT FORM AND REPRESENTATION
    4. PSEUDO-CUT STRATEGY
    5. DETERMINATION OF THE DISTANCE D BY EXPLOITING QUICK OBJECTIVE FUNCTION AND DIRECTIONAL EVALUATIONS
    6. CHOOSING THE POINTS X′ AND X″
    7. ADDITIONAL CONSIDERATIONS FOR CHOOSING XO
    8. CONCLUSION
    9. APPENDIX
  18. Chapter 13: Management of Bus Driver Duties Using Data Mining
    1. ABSTRACT
    2. INTRODUCTION
    3. THE BUS DRIVERS ALLOCATION PROBLEM IN RPTC
    4. DATA MINING AND THE ROUGH SET THEORY
    5. A MATHEMATICAL FORMULATION OF THE BDAP
    6. A BI-CRITERION NON-DOMINATED SOLUTION APPROACH FOR SOLVING THE BDAP
    7. AN APPROACH FOR THE EVALUATION OF THE DRIVER SATISFACTION LEVEL
    8. CASE STUDY
    9. CONCLUSION
  19. Chapter 14: A New Approach to Associative Classification Based on Binary Multi-Objective Particle Swarm Optimization
    1. ABSTRACT
    2. INTRODUCTION
    3. RELATED WORK
    4. ASSOCIATIVE CLASSIFICATION RULE MINING
    5. BMOPSO FOR ASSOCIATIVE CLASSIFICATION RULE MINING
    6. EXPERIMENTAL STUDY
    7. CONCLUSION
  20. Chapter 15: Extraction of Target User Group from Web Usage Data Using Evolutionary Biclustering Approach
    1. ABSTRACT
    2. INTRODUCTION
    3. RELATED WORK
    4. METHODS AND MATERIALS
    5. EXPERIMENTAL RESULTS AND ANALYSIS
    6. CONCLUSION
  21. Chapter 16: Dynamic Assignment of Crew Reserve in Airlines
    1. ABSTRACT
    2. INTRODUCTION
    3. ANALYSIS OF THE RESERVE ACTIVITIES ASSIGNMENT PROBLEM
    4. CONCLUSION
  22. Chapter 17: DIMMA-Implemented Metaheuristics for Finding Shortest Hamiltonian Path Between Iranian Cities Using Sequential DOE Approach for Parameters Tuning
    1. ABSTRACT
    2. INTRODUCTION
    3. LITERATURE REVIEW
    4. INITIATION PHASE
    5. BLUEPRINT PHASE
    6. CONSTRUCTION PHASE
    7. FINDING THE SHORTEST HAMILTONIAN PATH OF ALL IRANIAN CITIES
    8. CONCLUSION
  23. Compilation of References
  24. About the Contributors