Description

Book Synopsis
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.

Particle Swarm Optimization

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    A Hardback by Maurice Clerc

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      Book details

      Publisher ISTE Ltd and John Wiley & Sons Inc
      Published 7 February 2006
      ISBN-13 9781905209040
      978-1905209040
      ISBN-10 1905209045
      Also in:
      Optimization

      Description

      Book Synopsis
      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.

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