Handbook of metaheuristic algorithms from fundamental theories to advanced applications
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 t...
| Otros Autores: | , |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
London ; San Diego, CA :
Academic Press, an imprint of Elsevier
[2023]
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| Colección: | Uncertainty, computational techniques, and decision intelligence.
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009835434106719 |
| Sumario: | 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. |
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| Notas: | <b>PART 1 Fundamentals</b> <p>1. Introduction</p> <p>2. Optimization problems</p> <p>3. Traditional methods</p> <p>4. Metaheuristic algorithms</p> <p>5. Simulated annealing</p> <p>6. Tabu search</p> <p>7. Genetic algorithm</p> <p>8. Ant colony optimization</p> <p>9. Particle swarm optimization</p> <p>10. Differential evolution</p> <p><b>PART 2 Advanced technologies</b></p> <p>11. Solution encoding and initialization operator</p> <p>12. Transition operator</p> <p>13. Evaluation and determination operators</p> <p>14. Parallel metaheuristic algorithm</p> <p>15. Hybrid metaheuristic and hyperheuristic algorithms</p> <p>16. Local search algorithm</p> <p>17. Pattern reduction</p> <p>18. Search economics</p> <p>19. Advanced applications</p> <p>20. Conclusion and future research directions</p> <p>A. Interpretations and analyses of simulation results</p> <p>B. Implementation in Python</p> |
| Descripción Física: | 1 online resource (xxxviii, 584 pages) : illustrations (black & white) |
| Bibliografía: | Includes bibliographical references (pages 553-574) and index. |
| ISBN: | 9780443191091 |
