TY - JOUR
T1 - Comparative study of parallel variants for a Particle Swarm Optimization algorithm implemented on a multithreading GPU
AU - Laguna-Sánchez, Gerardo A.
AU - Olguín-Carbajal, Mauricio
AU - Cruz-Cortés, Nareli
AU - Barrón-Fernández, Ricardo
AU - Álvarez-Cedillo, Jesús A.
PY - 2009/12
Y1 - 2009/12
N2 - 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.
AB - 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.
KW - General-purpose GPU
KW - Global optimization
KW - Multithreading GPU
KW - PSO
KW - Parallel programming
UR - http://www.scopus.com/inward/record.url?scp=84860716659&partnerID=8YFLogxK
M3 - Artículo
SN - 1665-6423
VL - 7
SP - 292
EP - 309
JO - Journal of Applied Research and Technology
JF - Journal of Applied Research and Technology
IS - 3
ER -