Open AccessComparative Study of Parallel Variants for a Particle Swarm Optimization
Author(s) -
Gerardo LagunaSanchez,
Mauricio Olguín Carbajal,
Nareli Cruz-Cortés,
Ricardo BarrónFernández,
Jesús Antonio Álvarez Cedillo
Publication year - 2009
Publication title -
journal of applied research and technology
Language(s) - English
Resource type - Journals
ISSN - 2448-6736
DOI - 10.22201/icat.16656423.2009.7.03.489
Subject(s) - computer science , particle swarm optimization , multithreading , heuristic , multi swarm optimization , metaheuristic , thread (computing) , mathematical optimization , parallel computing , algorithm , artificial intelligence , mathematics , operating system
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.