Description
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.
Table of Contents
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.
