Intelligent Evolutionary Optimization introduces biologically-inspired intelligent optimization algorithms to address complex optimization problems and provide practical solutions for tackling combinatorial optimization problems. The book explores efficient search and optimization methods in high-dimensional spaces, particularly for high-dimensional multi-objective optimization problems, offering practical guidance and effective solutions across various domains. Providing practical solutions, methods, and tools to tackle complex optimization problems and enhance modern optimization techniques, this book will be a valuable resource for professionals seeking to enhance their understanding and proficiency in intelligent evolutionary optimization.
• Introduces biologically-inspired intelligent optimization algorithms capable of effectively solving complex optimization problems, teaching readers how to apply these algorithms and improve existing optimization techniques • Explores multi-objective optimization problems in high-dimensional spaces for readers to understand how to perform efficient search and optimization, acquiring strategies and tools adapted to high-dimensional environments • Presents the practical applications of intelligent evolutionary optimization in various fields to help readers gain insights into the latest trends and application scenarios in the field and receive practical guidance and solutions
1. 2.1 Introduction - p. 21 2. 2.2 Preliminaries - p. 22 3. 2.3 Proposed algorithm: θ-DEA - p. 25 4. 2.4 Experimental design - p. 34 5. 2.5 Experimental results - p. 42 6. 2.6 Conclusion - p. 63 7. References - p. 63 8. Chapter 3 Balancing convergence and diversity in decomposition-based many-objective optimizers - p. 67 9. Keywords - p. 67 10. Contents - p. 67 11. 3.1 Introduction - p. 67 12. 3.2 Preliminaries and background - p. 68 13. 3.3 Basic idea - p. 69 14. 3.4 Proposed algorithms - p. 72 15. 3.5 Experimental design - p. 79 16. 3.6 Analysis of the performance of enhanced algorithms - p. 85 17. 3.7 Comparison with state-of-the-art algorithms - p. 91 18. 3.8 Conclusion - p. 102 19. References - p. 103 20. Chapter 4 Objective reduction in many-objective optimization: evolutionary multiobjective approaches and comprehensive analysis - p. 107 21. Keywords - p. 107 22. Contents - p. 107 23. 4.1 Introduction - p. 108 24. 4.2 Preliminaries and background - p. 109 25. 4.3 Proposed multiobjective approaches - p. 115 26. 4.4 Analysis of dominance structure- and correlation-based approaches - p. 120 27. 4.5 Benchmark experiments - p. 125 28. 4.6 Applications to real-world problems - p. 142 29. 4.7 Benefits of the proposed approaches - p. 146 30. 4.8 Conclusion - p. 151 31. References - p. 151 32. Chapter 5 Expensive multiobjective evolutionary optimization assisted by dominance prediction - p. 155 33. Keywords - p. 155 34. Contents - p. 155 35. 5.1 Introduction - p. 155 36. 5.2 Preliminaries and background - p. 156 37. 5.3 The proposed algorithm - p. 160 38. 5.4 Experiments - p. 169 39. 5.5 Conclusion - p. 182 40. References - p. 185 41. Summary of part I - p. 187 42. Part II Heuristic algorithm for flexible job shop scheduling problem - p. 189 43. Chapter 6 Preliminary - p. 191 44. Chapter 7 A hybrid harmony search algorithm for flexible job shop scheduling problem - p. 201 45. Chapter 8 Flexible job shop scheduling using hybrid differential evolution algorithms - p. 231 46. Keywords - p. 231 47. Contents - p. 231 48. 8.1 Introduction - p. 231 49. 8.2 Basic differential evolution algorithm - p. 233 50. 8.3 Proposed hybrid differential evolution for the flexible job shop scheduling problem - p. 235 51. 8.4 Experimental studies - p. 249 52. 8.5 Conclusion - p. 268 53. References - p. 269 54. Chapter 9 An integrated search heuristic for large-scale flexible job shop scheduling problems - p. 271 55. Chapter 10 Multiobjective flexible job shop scheduling using memetic algorithms - p. 303 56. Keywords - p. 303 57. Contents - p. 303 58. 10.1 Introduction - p. 303 59. 10.2 Background - p. 305 60. 10.3 Overview of the proposed memetic algorithms - p. 310 61. 10.4 Exploration using genetic search - p. 311 62. 10.5 Exploitation using local search - p. 314 63. 10.6 Experimental studies - p. 321 64. 10.7 Conclusion - p. 340 65. 10.8 Summary of this part - p. 340 66. References - p. 341 67. Summary of part II - p. 345 68. Appendix A Symbol cross-reference table - p. 347 69. Index - p. 351
Intelligent Evolutionary Optimization introduces biologically-inspired intelligent optimization algorithms to address complex optimization problems and provide practical solutions for tackling combinatorial optimization problems. The book explores efficient search and optimization methods in high-dimensional spaces, particularly for high-dimensional multi-objective optimization problems, offering practical guidance and effective solutions across various domains. Providing practical solutions, methods, and tools to tackle complex optimization problems and enhance modern optimization techniques, this book will be a valuable resource for professionals seeking to enhance their understanding and proficiency in intelligent evolutionary optimization.
• Introduces biologically-inspired intelligent optimization algorithms capable of effectively solving complex optimization problems, teaching readers how to apply these algorithms and improve existing optimization techniques • Explores multi-objective optimization problems in high-dimensional spaces for readers to understand how to perform efficient search and optimization, acquiring strategies and tools adapted to high-dimensional environments • Presents the practical applications of intelligent evolutionary optimization in various fields to help readers gain insights into the latest trends and application scenarios in the field and receive practical guidance and solutions
1. 2.1 Introduction - p. 21 2. 2.2 Preliminaries - p. 22 3. 2.3 Proposed algorithm: θ-DEA - p. 25 4. 2.4 Experimental design - p. 34 5. 2.5 Experimental results - p. 42 6. 2.6 Conclusion - p. 63 7. References - p. 63 8. Chapter 3 Balancing convergence and diversity in decomposition-based many-objective optimizers - p. 67 9. Keywords - p. 67 10. Contents - p. 67 11. 3.1 Introduction - p. 67 12. 3.2 Preliminaries and background - p. 68 13. 3.3 Basic idea - p. 69 14. 3.4 Proposed algorithms - p. 72 15. 3.5 Experimental design - p. 79 16. 3.6 Analysis of the performance of enhanced algorithms - p. 85 17. 3.7 Comparison with state-of-the-art algorithms - p. 91 18. 3.8 Conclusion - p. 102 19. References - p. 103 20. Chapter 4 Objective reduction in many-objective optimization: evolutionary multiobjective approaches and comprehensive analysis - p. 107 21. Keywords - p. 107 22. Contents - p. 107 23. 4.1 Introduction - p. 108 24. 4.2 Preliminaries and background - p. 109 25. 4.3 Proposed multiobjective approaches - p. 115 26. 4.4 Analysis of dominance structure- and correlation-based approaches - p. 120 27. 4.5 Benchmark experiments - p. 125 28. 4.6 Applications to real-world problems - p. 142 29. 4.7 Benefits of the proposed approaches - p. 146 30. 4.8 Conclusion - p. 151 31. References - p. 151 32. Chapter 5 Expensive multiobjective evolutionary optimization assisted by dominance prediction - p. 155 33. Keywords - p. 155 34. Contents - p. 155 35. 5.1 Introduction - p. 155 36. 5.2 Preliminaries and background - p. 156 37. 5.3 The proposed algorithm - p. 160 38. 5.4 Experiments - p. 169 39. 5.5 Conclusion - p. 182 40. References - p. 185 41. Summary of part I - p. 187 42. Part II Heuristic algorithm for flexible job shop scheduling problem - p. 189 43. Chapter 6 Preliminary - p. 191 44. Chapter 7 A hybrid harmony search algorithm for flexible job shop scheduling problem - p. 201 45. Chapter 8 Flexible job shop scheduling using hybrid differential evolution algorithms - p. 231 46. Keywords - p. 231 47. Contents - p. 231 48. 8.1 Introduction - p. 231 49. 8.2 Basic differential evolution algorithm - p. 233 50. 8.3 Proposed hybrid differential evolution for the flexible job shop scheduling problem - p. 235 51. 8.4 Experimental studies - p. 249 52. 8.5 Conclusion - p. 268 53. References - p. 269 54. Chapter 9 An integrated search heuristic for large-scale flexible job shop scheduling problems - p. 271 55. Chapter 10 Multiobjective flexible job shop scheduling using memetic algorithms - p. 303 56. Keywords - p. 303 57. Contents - p. 303 58. 10.1 Introduction - p. 303 59. 10.2 Background - p. 305 60. 10.3 Overview of the proposed memetic algorithms - p. 310 61. 10.4 Exploration using genetic search - p. 311 62. 10.5 Exploitation using local search - p. 314 63. 10.6 Experimental studies - p. 321 64. 10.7 Conclusion - p. 340 65. 10.8 Summary of this part - p. 340 66. References - p. 341 67. Summary of part II - p. 345 68. Appendix A Symbol cross-reference table - p. 347 69. Index - p. 351
