Velocity Adaptation in Particle Swarm Optimization

Article in Edited Volumes


Publication Details

Author(s): Helwig S, Neumann F, Wanka R
Title edited volumes: Handbook of Swarm Intelligence
Publisher: Springer
Publishing place: Heidelberg
Publication year: 2011
Title of series: Adaptation, Learning, and Optimization (ALO)
Volume: 8
Pages range: 155-173


Abstract


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 Authors / FAU Editors

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


External institutions with authors

Max Planck Institute for Informatics / Max-Planck-Institut für Informatik


How to cite

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

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

BibTeX: 

Last updated on 2019-13-01 at 07:08