Runtime analysis of binary PSO

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Runtime analysis of binary PSO
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 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 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. Categories and Subject Descriptors F.2 [Theory of Computation]: Anal...
Dirk Sudholt, Carsten Witt
Added 09 Nov 2010
Updated 09 Nov 2010
Type Conference
Year 2008
Authors Dirk Sudholt, Carsten Witt
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