Samenvatting
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.
Specificaties
Inhoudsopgave
<p>Introduction.</p>
<p>Part 1: Particle Swarm Optimization.</p>
<p>Chapter 1. What is a difficult problem?</p>
<p>Chapter 2. On a table corner.</p>
<p>Chapter 3. First formulations.</p>
<p>Chapter 4. Benchmark set.</p>
<p>Chapter 5. Mistrusting chance.</p>
<p>Chapter 6. First results.</p>
<p>Chapter 7. Swarm: memory and influence graphs.</p>
<p>Chapter 8. Distributions of proximity.</p>
<p>Chapter 9. Optimal parameter settings.</p>
<p>Chapter 10. Adaptations.</p>
<p>Chapter 11. TRIBES or co–operation of tribes.</p>
<p>Chapter 12. On the constraints.</p>
<p>Chapter 13. Problems and applications.</p>
<p>Chapter 14. Conclusion.</p>
<p>Part 2: Outlines.</p>
<p>Chapter 15. On parallelism.</p>
<p>Chapter 16. Combinatorial problems.</p>
<p>Chapter 17. Dynamics of a swarm.</p>
<p>Chapter 18. Techniques and alternatives.</p>
<p>Further Information.</p>
<p>Bibliography.</p>
<p>Index.</p>