THE PARTICLE SWARM AS COLLABORATIVE SAMPLING OF THE SEARCH SPACE

James Kennedy ()
Additional contact information
James Kennedy: US Bureau of Labor Statistics, Washington, DC, 20212, USA

Advances in Complex Systems (ACS), 2007, vol. 10, issue supp0, 191-213

Abstract: The particle swarm algorithm uses principles derived from social psychology to find optimal points in a search space. The present paper decomposes and reinterprets the particle swarm in order to discover new ways of implementing the algorithm. Some essential characteristics of the method are illuminated, and some inessential features are discarded. Various new forms are tested and found to perform well on a suite of test functions. In particular, it is shown that the traditional trajectory formulas can be replaced with random number generators sampling from various symmetrical probability distributions. The excellent performance of these new versions demonstrates that the strength of the algorithm is in the interactions of the particles, rather than in their behavior as individuals.

Keywords: Swarm; social simulation; optimization (search for similar items in EconPapers)
Date: 2007
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0219525907001070
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:acsxxx:v:10:y:2007:i:supp0:n:s0219525907001070

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0219525907001070

Access Statistics for this article

Advances in Complex Systems (ACS) is currently edited by Frank Schweitzer

More articles in Advances in Complex Systems (ACS) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().