Velocity Adaptation in Particle Swarm Optimization

Beitrag in einem Sammelwerk

Details zur Publikation

Autor(en): Helwig S, Neumann F, Wanka R
Titel Sammelwerk: Handbook of Swarm Intelligence
Verlag: Springer
Verlagsort: Heidelberg
Jahr der Veröffentlichung: 2011
Titel der Reihe: Adaptation, Learning, and Optimization (ALO)
Band: 8
Seitenbereich: 155-173


Swarm Intelligence methods have been shown to produce good results in various problem domains. A well-known method belonging to this kind of algorithms is particle swarm optimization (PSO). In this chapter, we examine how adaptation mechanisms can be used in PSO algorithms to better deal with continuous optimization problems. In case of bound-constrained optimization problems, one has to cope with the situation that particles may leave the feasible search space. To deal with such situations, different bound handling methods were proposed in the literature, and it was observed that the success of PSO algorithms highly depends on the chosen bound handling method. We consider how velocity adaptation mechanisms can be used to cope with bounded search spaces. Using this approach we show that the bound handling method becomes less important for PSO algorithms and that using velocity adaptation leads to better results for a wide range of benchmark functions.

FAU-Autoren / FAU-Herausgeber

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

Autor(en) der externen Einrichtung(en)
Max Planck Institute for Informatics / Max-Planck-Institut für Informatik


Helwig, S., Neumann, F., & Wanka, R. (2011). Velocity Adaptation in Particle Swarm Optimization. In Handbook of Swarm Intelligence. (pp. 155-173). Heidelberg: Springer.

Helwig, Sabine, Frank Neumann, and Rolf Wanka. "Velocity Adaptation in Particle Swarm Optimization." Handbook of Swarm Intelligence. Heidelberg: Springer, 2011. 155-173.


Zuletzt aktualisiert 2019-13-01 um 07:08