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Modeling, Analysis, and Applications in Metaheuristic Computing

Book Description

The engineering and business problems the world faces today have become more impenetrable and unstructured, making the design of a satisfactory problem-specific algorithm nontrivial.
Modeling, Analysis, and Applications in Metaheuristic Computing: Advancements and Trends is a collection of the latest developments, models, and applications within the transdisciplinary fields related to metaheuristic computing. Providing researchers, practitioners, and academicians with insight into a wide range of topics such as genetic algorithms, differential evolution, and ant colony optimization, this book compiles the latest findings, analysis, improvements, and applications of technologies within metaheuristic computing.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Editorial Advisory Board and List of Reviewers
    1. EDITORIAL ADVISORY BOARD
    2. INTERNATIONAL EDITORIAL REVIEW BOARD
    3. HONORARY EDITOR
  5. Preface
    1. ABSTRACT
    2. TOWARDS MORE EFFECTIVE METAHEURISTIC COMPUTING
    3. 1. INTRODUCTION
    4. 2. NATURE-INSPIRED COMPUTATION VS. STRATEGIC LEVEL PROBLEM-SOLVING
    5. 3. METAHEURISTICS HYBRIDIZATIONS
    6. 4. UNIFIED DEVELOPMENT FRAMEWORK
    7. 5. CONCLUSIONS
  6. Chapter 1: Metaheuristic Search with Inequalities and Target Objectives for Mixed Binary Optimization Part I
    1. ABSTRACT
    2. 1. NOTATION AND PROBLEM FORMULATION
    3. 2. EXPLOITING INEQUALITIES IN TARGET SOLUTION STRATEGIES
    4. 3. GENERALIZATION TO INCLUDE PARTIAL VECTORS AND MORE GENERAL TARGET OBJECTIVES
    5. 4. STRONGER INEQUALITIES AND ADDITIONAL VALID INEQUALITIES FROM BASIC FEASIBLE LP SOLUTIONS
    6. 5. GENERATING TARGET OBJECTIVES AND SOLUTIONS BY EXPLOITING PROXIMITY
    7. 6. CONCLUSION
  7. Chapter 2: Metaheuristic Search with Inequalities and Target Objectives for Mixed Binary Optimization – Part II
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. TARGET OBJECTIVES BY EXPLOITING PROXIMITY WITH EMBEDDED MEMORY
    4. 3. GENERATING TARGET OBJECTIVES AND SOLUTIONS BY EXPLOITING REACTION AND RESISTANCE
    5. 4. CREATING AND MANAGING THE TABU LIST T – RESISTANCE & REACTION PROCEDURE COMPLETED
    6. 5. INTENSIFICATION AND DIVERSIFICATION BASED ON STRATEGIC INEQUALITIES
    7. 6. CONCLUSION
  8. Chapter 3: A Reinforcement Learning
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 3. THE EXAMINATION TIMETABLING PROBLEM
    4. 4. THE REINFORCEMENT LEARNING – GREAT DELUGE HYPER-HEURISTIC
    5. 6. CONCLUSION
  9. Chapter 4: A Hybrid Genetic Algorithm for Optimization of Two-Dimensional Cutting-Stock Problem
    1. ABSTRACT
    2. INTRODUCTION
    3. PROBLEM FORMULATION
    4. THE GENETIC ALGORITHM
    5. CONCLUSION
  10. Chapter 5: A Rigorous Analysis of the Harmony Search Algorithm
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. HARMONY SEARCH
    4. 3. COMPARISON TO EVOLUTION STRATEGIES
    5. 4. REPRESENTATIVE PUBLICATIONS IN DETAIL
    6. 5. DISCUSSION
    7. 6. CONCLUSION
  11. Chapter 6: Research Commentary Survival of the Fittest Algorithm or the Novelest Algorithm?
    1. ABSTRACT
    2. INTRODUCTION
    3. DISCUSSIONS AND CONCLUSIONS
  12. Chapter 7: DIMMA
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. ARCHITECTURE OF DIMMA
    4. 3. INITIATION PHASE
    5. 4 BLUEPRINT PHASE
    6. 5. CONSTRUCTION PHASE
    7. 6. A CASE STUDY
    8. CONCLUSION
  13. Chapter 8: Movement Strategies for Multi-Objective Particle Swarm Optimization
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. BACKGROUND INFORMATION
    4. 3. MOVEMENT STRATEGIES
    5. 5. REAL-WORLD APPLICATIONS
    6. 6. CONCLUSION
  14. Chapter 9: A New Multiagent Algorithm for Dynamic Continuous Optimization
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. BENCHMARK SET FOR DYNAMIC OPTIMIZATION
    4. 3. THE PROPOSED MADO ALGORITHM
    5. 4. RESULTS AND DISCUSSION
    6. 5. CONCLUSION
  15. Chapter 10: Improving Switched Current Sigma Delta Modulators’ Performances via the Particle Swarm Optimization Technique
    1. ABSTRACT
    2. INTRODUCTION
    3. BASIC CONCEPTS OF OVERSAMPLED ΣΔ MODULATORS
    4. OPTIMIZATION AND SIMULATION RESULTS
    5. CONCLUSION
  16. Chapter 11: A Reinforced Tabu Search Approach for 2D Strip Packing
    1. ABSTRACT
    2. INTRODUCTION
    3. 1. PROBLEM FORMULATION
    4. 2. LITERATURE REVIEW
    5. 3. CTS: A CONSISTENT TABU SEARCH FOR 2D-SPP
    6. 4. EXPERIMENTATIONS
    7. 5. CONCLUSION
  17. Chapter 12: A Study of Tabu Search for Coloring Random 3-Colorable Graphs Around the Phase Transition
    1. ABSTRACT
    2. INTRODUCTION
    3. RANDOM GRAPHS
    4. TC: A TABU SEARCH ALGORITHM FOR 3-COL
    5. TC ALGORITHM
    6. COMPUTATIONAL RESULTS
    7. INFLUENCE OF THE EDGE PROBABILITY P ON THE PROBLEM DIFFICULTY
    8. DEEPER EXPERIMENTS AROUND THE PHASE TRANSITIONS
    9. INFLUENCE OF THE PROBLEM SIZE N ON THE PROBLEM DIFFICULTY
    10. IMPACT OF LONGER RUNS ON THE SOLUTION PERFORMANCE
    11. HOW FAR CAN WE GO WITH TC?
    12. CONCLUSION
    13. OUTSIDE OF THE PHASE TRANSITION THRESHOLDS
    14. AROUND THE PHASE TRANSITION THRESHOLDS
    15. PHASE TRANSITION THRESHOLDS
  18. Chapter 13: A Metaheuristic Approach to the Graceful Labeling Problem
    1. ABSTRACT
    2. GRACEFUL LABELING PROBLEM
    3. ANT COLONY OPTIMIZATION
    4. ACO-BASED ALGORITHM FOR THE GRACEFUL LABELING PROBLEM
    5. FUTURE RESEARCH DIRECTIONS
    6. CONCLUSION
  19. Chapter 14: A Survey on Evolutionary Instance Selection and Generation
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. PRELIMINARIES
    4. 3. EVOLUTIONARY PROTOTYPE SELECTION
    5. 4. EVOLUTIONARY PROTOTYPE GENERATION
    6. 5. EVOLUTIONARY TRAINING SET SELECTION
    7. 6. CONCLUSION
    8. APPENDIX: ACRONYMS TABLE
  20. Chapter 15: A Sociopsychological Perspective on Collective Intelligence in Metaheuristic Computing
    1. ABSTRACT
    2. INTRODUCTION
    3. COMPUTATIONAL INTELLIGENCE FOUNDATIONS OF METAHEURISTIC COMPUTING
    4. COLLECTIVE BEHAVIORS OF COLLECTIVE INTELLIGENCE
    5. COGNITIVE SOCIOPSYCHOLOGY OF COLLECTIVE INTELLIGENCE
    6. THE INTERACTIVE BEHAVIORAL MODEL OF COLLECTIVE INTELLIGENCE
    7. THE COGNITIVE PROCESS OF PROBLEM SOLVING FOR METAHUERISTIC COMPUTING
    8. CONCLUSION
  21. Chapter 16: Theorems Supporting r-flip Search for Pseudo-Boolean Optimization
    1. ABSTRACT
    2. INTRODUCTION
    3. PSEUDO-BOOLEAN OPTIMIZATION
    4. SPECIAL CASES
    5. COMPUTATIONAL EXPERIENCE
    6. CONCLUSION
    7. APPENDIX A: THEOREM PROOFS
    8. APPENDIX B: A FILTER AND FAN PROCEDURE
  22. Chapter 17: Stochastic Learning for SAT- Encoded Graph Coloring Problems
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. RELATED WORK
    4. 3. FINITE LEARNING AUTOMATA FOR SAT-ENCODED GCP
    5. 5. CONCLUSION
  23. Chapter 18: Page Number and Graph Treewidth
    1. ABSTRACT
    2. INTRODUCTION
    3. 1. PRELIMINARIES
    4. 2. THE MAIN RESULTS
    5. CONCLUSION
  24. Chapter 19: The Analysis of Zero Inventory Drift Variants Based on Simple and General Order-Up-To Policies
    1. ABSTRACT
    2. INTRODUCTION
    3. 2. LITERATURE REVIEW
    4. 3. REPLENISHMENT POLICIES
    5. 4. THE SHORTCOMINGS OF REPLENISHMENT POLICIES
    6. 5. ZERO INVENTORY DRIFT VARIANTS
    7. 6. STABILITY ANALYSIS
    8. 7. SIMULATION EXPERIMENT
    9. 8. CONCLUSION
  25. Chapter 20: BDD-Based Synthesis of Reversible Logic
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. PRELIMINARIES
    4. 3. GENERAL IDEA
    5. 4. EXPLOITING BDD OPTIMIZATION
    6. 5. THEORETICAL CONSIDERATION
    7. 6. EXPERIMENTAL RESULTS
    8. 7. CONCLUSION
  26. Compilation of References
  27. About the Contributors
  28. Index