Particle Swarm 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.

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.

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. 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.