Linked Life Data

20371407

Source:http://linkedlifedata.com/resource/pubmed/id/20371407

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
6
pubmed:dateCreated
2010-11-16
pubmed:abstractText
In recent years, there has been a growing interest in the study of particle swarm optimization (PSO) in dynamic environments. This paper presents a new PSO model, called PSO with composite particles (PSO-CP), to address dynamic optimization problems. PSO-CP partitions the swarm into a set of composite particles based on their similarity using a "worst first" principle. Inspired by the composite particle phenomenon in physics, the elementary members in each composite particle interact via a velocity-anisotropic reflection scheme to integrate valuable information for effectively and rapidly finding the promising optima in the search space. Each composite particle maintains the diversity by a scattering operator. In addition, an integral movement strategy is introduced to promote the swarm diversity. Experiments on a typical dynamic test benchmark problem provide a guideline for setting the involved parameters and show that PSO-CP is efficient in comparison with several state-of-the-art PSO algorithms for dynamic optimization problems.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
1941-0492
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
40
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1634-48
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Particle swarm optimization with composite particles in dynamic environments.
pubmed:affiliation
College of Information Science and Engineering, Northeastern University, Shenyang 110004, China. liulili@ise.neu.edu.cn
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't