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dc.contributor.authorSudholt, Dirkde
dc.contributor.authorWitt, Carstende
dc.description.abstractWe 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.relation.ispartofseriesReihe CI; 241-08de
dc.titleRuntime analysis of binary PSOen
dcterms.accessRightsopen access-
Appears in Collections:Sonderforschungsbereich (SFB) 531

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