Particle Swarm Optimization Almost Surely Finds Local Optima

Beitrag bei einer Tagung


Details zur Publikation

Autor(en): Schmitt M, Wanka R
Titel Sammelwerk: Theoretical Computer Science
Verlag: Elsevier
Jahr der Veröffentlichung: 2013
Tagungsband: Proc. 15th Genetic and Evolutionary Computation Conference
Seitenbereich: 1629-1636
ISSN: 0304-3975


Abstract


Particle swarm optimization (PSO) is a popular nature-inspired meta-heuristic for solving continuous optimization problems. Although this technique is widely used, up to now only some partial aspects of the method have been formally investigated. In particular, while it is well-studied how to let the swarm converge to a single point in the search space, no general theoretical statements about this point or on the best position any particle has found have been known. For a very general class of objective functions, we provide for the first time results about the quality of the solution found. We show that a slightly adapted PSO almost surely finds a local optimum. To do so, we investigate the newly defined . potential of the swarm. The potential drops when the swarm approaches the point of convergence, but increases if the swarm remains close to a point that is not a local optimum, meaning that the swarm charges potential and continues its movement.



FAU-Autoren / FAU-Herausgeber

Schmitt, Manuel
Professur für Informatik (Effiziente Algorithmen und Kombinatorische Optimierung)
Wanka, Rolf Prof. Dr.
Professur für Informatik (Effiziente Algorithmen und Kombinatorische Optimierung)


Zitierweisen

APA:
Schmitt, M., & Wanka, R. (2013). Particle Swarm Optimization Almost Surely Finds Local Optima. In Proc. 15th Genetic and Evolutionary Computation Conference (pp. 1629-1636). Amsterdam, NL: Elsevier.

MLA:
Schmitt, Manuel, and Rolf Wanka. "Particle Swarm Optimization Almost Surely Finds Local Optima." Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), Amsterdam Elsevier, 2013. 1629-1636.

BibTeX: 

Zuletzt aktualisiert 2018-09-08 um 23:11