Particle Swarm Optimizers for Pareto Optimization with Enhanced Archiving Techniques

Bartz-Beielstein, Thomas; Limbourg, Philipp; Mehnen, Jörn; Parsopoulos, Konstantinos E.; Schmitt, Karlheinz; Vrahatis, Michael N.

Particle Swarm Optimizers for Pareto Optimization with Enhanced Archiving Techniques

dc.contributor.author	Bartz-Beielstein, Thomas	de
dc.contributor.author	Limbourg, Philipp	de
dc.contributor.author	Mehnen, Jörn	de
dc.contributor.author	Parsopoulos, Konstantinos E.	de
dc.contributor.author	Schmitt, Karlheinz	de
dc.contributor.author	Vrahatis, Michael N.	de
dc.date.accessioned	2004-12-07T08:21:35Z
dc.date.available	2004-12-07T08:21:35Z
dc.date.created	2003	de
dc.date.issued	2003-12-23	de
dc.description.abstract	During the last decades, numerous heuristic search methods for solving multi-objective optimization problems have been developed. Population oriented approaches such as evolutionary algorithms and particle swarm optimization can be distinguished into the class of archive-based algorithms and algorithms without archive. While the latter may lose the best solutions found so far, archive based algorithms keep track of these solutions. In this article a new particle swarm optimization technique, called DOPS, for multiobjective optimization problems is proposed. DOPS integrates well-known archiving techniques from evolutionary algorithms into particle swarm optimization. Modifications and extensions of the archiving techniques are empirically analyzed and several test functions are used to illustrate the usability of the proposed approach. A statistical analysis of the obtained results is presented. The article concludes with a discussion of the obtained results as well as ideas for further research.	en
dc.format.extent	926720 bytes
dc.format.mimetype	application/pdf
dc.identifier.uri	http://hdl.handle.net/2003/5443
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-14903
dc.language.iso	en	de
dc.publisher	Universität Dortmund	de
dc.relation.ispartofseries	Reihe Computational Intelligence ; 153	de
dc.subject.ddc	004	de
dc.title	Particle Swarm Optimizers for Pareto Optimization with Enhanced Archiving Techniques	en
dc.type	Text	de
dc.type.publicationtype	report
dcterms.accessRights	open access

Files

Original bundle

Now showing 1 - 1 of 1

Name:: 153.pdf
Size:: 905 KB
Format:: Adobe Portable Document Format
Description:: DNB

Download

Collections

Sonderforschungsbereich (SFB) 531