MRPSO: MapReduce Particle Swarm Optimization
Venue
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2007), IEEE
Publication Year
2007
Authors
Andrew W. McNabb, Christopher K. Monson, Kevin D. Seppi
BibTeX
Abstract
Abstract— In optimization problems involving large amounts of data, such as web
content, commercial transaction information, or bioinformatics data, individual
function evaluations may take minutes or even hours. Particle Swarm Optimization
(PSO) must be parallelized for such functions. However, large-scale parallel
programs must communicate efficiently, balance work across all processors, and
address problems such as failed nodes. We present MapReduce Particle Swarm
Optimization (MRPSO), a PSO implementation based on the MapReduce parallel
programming model. We describe MapReduce and show how PSO can be naturally
expressed in this model, without explicitly addressing any of the details of
parallelization. We present a benchmark function for evaluating MRPSO and note that
MRPSO is not appropriate for optimizing easily evaluated functions. We demonstrate
that MRPSO scales to 256 processors on moderately difficult problems and tolerates
node failures.
