Otimização por enxame de partículas em arquiteturas paralelas de alto desempenho.

Abstract

Particle Swarm Optimization (PSO) is an optimization technique that is used to solve many problems in different applications. However, most implementations are sequential. The optimization process requires a large number of evaluations of the objective function, especially in complex problems, involving a large amount of particles and dimensions. As a result, the algorithm may become inefficient in terms of performance, corresponding to the gain obtained with the implementation of improvement, execution time and even the quality of the expected result. To overcome these difficulties, high performance computing and parallel algorithms can be used, taking into account the characteristics of the architecture. This should increase performance, minimize response time and may even improve the quality of the final result. This paper presents a par-allel version of PSO implemented in a multiprocessor, multicomputer and Graphics Processing Unit (GPU) based paralle architectures. The different performed assessments show that the multicomputer achieved the best results for problems with high number of particles and dimensions. In terms of optimization, all parallel implementations achieved equal or higher efficiency in comparison to sequential implementation.