Runtime analysis of binary PSO

dc.contributor.author	Sudholt, Dirk	de
dc.contributor.author	Witt, Carsten	de
dc.date.accessioned	2009-05-12T16:01:46Z
dc.date.available	2009-05-12T16:01:46Z
dc.date.issued	2008-02	de
dc.description.abstract	We investigate the runtime of the Binary Particle Swarm Optimization (PSO) algorithm introduced by Kennedy and Eberhart (1997). The Binary PSO maintains a global best solution and a swarm of particles. Each particle consists of a current position, an own best position and a velocity vector used in a probabilistic process to update the particle s position. We present lower bounds for a broad class of implementations with swarms of polynomial size. To prove upper bounds, we transfer a fitness-level argument well-established for evolutionary algorithms (EAs) to PSO. This method is then applied to estimate the expected runtime on the class of unimodal functions. A simple variant of the Binary PSO is considered in more detail. The 1-PSO only maintains one particle, hence own best and global best solutions coincide. Despite its simplicity, the 1-PSO is surprisingly efficient. A detailed analysis for the function OneMax shows that the 1-PSO is competitive to EAs.	en
dc.identifier.uri	http://hdl.handle.net/2003/26148
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-9031
dc.language.iso	en	de
dc.relation.ispartofseries	Reihe CI; 241-08	de
dc.subject.ddc	004	de
dc.title	Runtime analysis of binary PSO	en
dc.type	Text	de
dc.type.publicationtype	report	de
dcterms.accessRights	open access

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