Global numerical optimization with a bi-population particle swarm optimizer
This paper presents an enhanced Particle Swarm Optimizer approach, which is designed to solve numerical unconstrained optimization problems. The approach incorporates a dual population in an attempt to overcome the problem of premature convergence to local optima. The proposed algorithm is validated...
Guardado en:
| Autores principales: | , |
|---|---|
| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
2007
|
| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/23180 |
| Aporte de: |
| Sumario: | This paper presents an enhanced Particle Swarm Optimizer approach, which is designed to solve numerical unconstrained optimization problems. The approach incorporates a dual population in an attempt to overcome the problem of premature convergence to local optima. The proposed algorithm is validated using standard test functions (unimodal, multi-modal, separable and nonseparable) taken from the specialized literature. The results are compared with values obtained by an algorithm representative of the state-of-the-art in the area. Our preliminary results indicate that our proposed approach is a competitive alternative to solve global optimization problems. |
|---|