Handbook of Metaheuristic Algorithms: From Fundamental Theories to Advanced Applications provides a brief introduction to metaheuristic algorithms from the ground up, including basic ideas and advanced solutions. Although readers may be able to find source code for some metaheuristic algorithms on the Internet, the coding styles and explanations are generally quite different, and thus requiring expanded knowledge between theory and implementation. This book can also help students and researchers construct an integrated perspective of metaheuristic and unsupervised algorithms for artificial intelligence research in computer science and applied engineering domains.
Metaheuristic algorithms can be considered the epitome of unsupervised learning algorithms for the optimization of engineering and artificial intelligence problems, including simulated annealing (SA), tabu search (TS), genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), differential evolution (DE), and others. Distinct from most supervised learning algorithms that need labeled data to learn and construct determination models, metaheuristic algorithms inherit characteristics of unsupervised learning algorithms used for solving complex engineering optimization problems without labeled data, just like self-learning, to find solutions to complex problems.
1. Chapter One : Introduction - p. Page xxx 2. Chapter Two : Optimization problems - p. Page 11 3. Chapter Three : Traditional methods - p. Page 27 4. Chapter Four : Metaheuristic algorithms - p. Page 69 5. Chapter Five : Simulated annealing - p. Page 78 6. Chapter Six : Tabu search - p. Page 93 7. Chapter Seven : Genetic algorithm - p. Page 109 8. Chapter Eight : Ant colony optimization - p. Page 138 9. Chapter Nine : Particle swarm optimization - p. Page 161 10. Chapter Ten : Differential evolution - p. Page 184 11. Chapter Eleven : Solution encoding and initialization operator - p. Page 209 12. Chapter Twelve : Transition operator - p. Page 227 13. Chapter Thirteen : Evaluation and determination operators - p. Page 257 14. Chapter Fourteen : Parallel metaheuristic algorithm - p. Page 287 15. Chapter Fifteen : Hybrid metaheuristic and hyperheuristic algorithms - p. Page 320 16. Chapter Sixteen : Local search algorithm - p. Page 350 17. Chapter Seventeen : Pattern reduction - p. Page 374 18. Chapter Eighteen : Search economics - p. Page 403 19. Chapter Nineteen : Advanced applications - p. Page 433 20. Chapter Twenty : Conclusion and future research directions - p. Page 457 21. Appendix A : Interpretations and analyses of simulation results - p. Page 469 22. Appendix B : Implementation in Python - p. Page 486 23. References - p. Page 551 24. Index - p. Page 551
Handbook of Metaheuristic Algorithms: From Fundamental Theories to Advanced Applications provides a brief introduction to metaheuristic algorithms from the ground up, including basic ideas and advanced solutions. Although readers may be able to find source code for some metaheuristic algorithms on the Internet, the coding styles and explanations are generally quite different, and thus requiring expanded knowledge between theory and implementation. This book can also help students and researchers construct an integrated perspective of metaheuristic and unsupervised algorithms for artificial intelligence research in computer science and applied engineering domains.
Metaheuristic algorithms can be considered the epitome of unsupervised learning algorithms for the optimization of engineering and artificial intelligence problems, including simulated annealing (SA), tabu search (TS), genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), differential evolution (DE), and others. Distinct from most supervised learning algorithms that need labeled data to learn and construct determination models, metaheuristic algorithms inherit characteristics of unsupervised learning algorithms used for solving complex engineering optimization problems without labeled data, just like self-learning, to find solutions to complex problems.
1. Chapter One : Introduction - p. Page xxx 2. Chapter Two : Optimization problems - p. Page 11 3. Chapter Three : Traditional methods - p. Page 27 4. Chapter Four : Metaheuristic algorithms - p. Page 69 5. Chapter Five : Simulated annealing - p. Page 78 6. Chapter Six : Tabu search - p. Page 93 7. Chapter Seven : Genetic algorithm - p. Page 109 8. Chapter Eight : Ant colony optimization - p. Page 138 9. Chapter Nine : Particle swarm optimization - p. Page 161 10. Chapter Ten : Differential evolution - p. Page 184 11. Chapter Eleven : Solution encoding and initialization operator - p. Page 209 12. Chapter Twelve : Transition operator - p. Page 227 13. Chapter Thirteen : Evaluation and determination operators - p. Page 257 14. Chapter Fourteen : Parallel metaheuristic algorithm - p. Page 287 15. Chapter Fifteen : Hybrid metaheuristic and hyperheuristic algorithms - p. Page 320 16. Chapter Sixteen : Local search algorithm - p. Page 350 17. Chapter Seventeen : Pattern reduction - p. Page 374 18. Chapter Eighteen : Search economics - p. Page 403 19. Chapter Nineteen : Advanced applications - p. Page 433 20. Chapter Twenty : Conclusion and future research directions - p. Page 457 21. Appendix A : Interpretations and analyses of simulation results - p. Page 469 22. Appendix B : Implementation in Python - p. Page 486 23. References - p. Page 551 24. Index - p. Page 551
