| Resumen |
The Particle Swarm Optimization (PSO) algorithm is a well known alternative for global optimization based on a bio?inspired
heuristic. PSO has good performance, low computational complexity and few parameters. Heuristic techniques have been widely studied in the last twenty years and the scientific community is still interested in technological alternatives that
accelerate these algorithms in order to apply them to bigger and more complex problems. This article presents an empirical
study of some parallel variants for a PSO algorithm, implemented on a Graphic Process Unit (GPU) device with multi?thread
support and using the most recent model of parallel programming for these cases. The main idea is to show that, with the help
of a multithreading GPU, it is possible to significantly improve the PSO algorithm performance by means of a simple and almost
straightforward parallel programming, getting the computing power of cluster in a conventional personal computer. |
