This is the first book devoted entirely to Particle Swarm Optimization (PSO), which is a non-specific algorithm, similar to evolutionary algorithms, such as taboo search and ant colonies. Since its original development in 1995, PSO has mainly been applied to continuous-discrete heterogeneous strongly non-linear numerical optimization and it is thus used almost everywhere in the world. Its convergence rate also makes it a preferred tool in dynamic optimization.
This is the first book devoted entirely to Particle Swarm Optimization (PSO), which is a non-specific algorithm, similar to evolutionary algorithms, such as taboo search and ant colonies. Since its original development in 1995, PSO has mainly been applied to continuous-discrete heterogeneous strongly non-linear numerical optimization and it is thus used almost everywhere in the world. Its convergence rate also makes it a preferred tool in dynamic optimization.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Maurice Clerc is recognized as one of the foremost PSO specialists in the world. A former France Telecom Research and Development engineer, he maintains his research activities as a consultant for the XPS (eXtended Particle Swarm) project.
Inhaltsangabe
Foreword. Introduction. Part 1: Particle Swarm Optimization. Chapter 1. What is a difficult problem? Chapter 2. On a table corner. Chapter 3. First formulations. Chapter 4. Benchmark set. Chapter 5. Mistrusting chance. Chapter 6. First results. Chapter 7. Swarm: memory and influence graphs. Chapter 8. Distributions of proximity. Chapter 9. Optimal parameter settings. Chapter 10. Adaptations. Chapter 11. TRIBES or co-operation of tribes. Chapter 12. On the constraints. Chapter 13. Problems and applications. Chapter 14. Conclusion. Part 2: Outlines. Chapter 15. On parallelism. Chapter 16. Combinatorial problems. Chapter 17. Dynamics of a swarm. Chapter 18. Techniques and alternatives. Further Information. Bibliography. Index.
Foreword. Introduction. Part 1: Particle Swarm Optimization. Chapter 1. What is a difficult problem? Chapter 2. On a table corner. Chapter 3. First formulations. Chapter 4. Benchmark set. Chapter 5. Mistrusting chance. Chapter 6. First results. Chapter 7. Swarm: memory and influence graphs. Chapter 8. Distributions of proximity. Chapter 9. Optimal parameter settings. Chapter 10. Adaptations. Chapter 11. TRIBES or co-operation of tribes. Chapter 12. On the constraints. Chapter 13. Problems and applications. Chapter 14. Conclusion. Part 2: Outlines. Chapter 15. On parallelism. Chapter 16. Combinatorial problems. Chapter 17. Dynamics of a swarm. Chapter 18. Techniques and alternatives. Further Information. Bibliography. Index.
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