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...

• Type: Conference
• Year: 2008
• Where: GECCO
• Authors: Dirk Sudholt, Carsten Witt
• Keywords: Binary Particle, Binary PSO, GECCO 2008, Optimization, Particle Swarm Optimization