Runtime analysis of a binary particle swarm optimizer

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Runtime analysis of a binary particle swarm optimizer
We investigate the runtime of a Binary Particle Swarm Optimizer (PSO) for optimizing pseudo-Boolean functions f : {0, 1}n → R. The Binary PSO maintains a swarm of particles searching for good solutions. Each particle consists of a current position from {0, 1}n , an own best position and a velocity vector used in a probabilistic process to update its current position. The velocities for a particle are then updated in the direction of its own best position and the position of the best particle in the swarm. We present a lower bound for the time needed to optimize any function with unique optimum. 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 on the test function OneMax, showing that there the Binary PSO is competitive to EAs. An additional experimental compar...
Dirk Sudholt, Carsten Witt
Added 30 Jan 2011
Updated 30 Jan 2011
Type Journal
Year 2010
Where TCS
Authors Dirk Sudholt, Carsten Witt
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