Comparative Study of Parallel Variants for a Particle Swarm Optimization
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...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2009 |
| País: | México |
| Institución: | UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO |
| Repositorio: | Journal of Applied Research and Technology |
| Idioma: | inglés |
| OAI Identifier: | oai:ojs2.localhost:article/489 |
| Acceso en línea: | https://jart.icat.unam.mx/index.php/jart/article/view/489 |
| Access Level: | acceso abierto |
| Palabra clave: | Multithreading GPU PSO general?purpose GPU parallel programming global optimization GPU con capacidad miltihilos GPU para propósitos generales programación paralela optimización global |
| Sumario: | 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. |
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