Particle Swarm with graphics hardware acceleration and local pattern search on bound constrained problems

This paper presents a particle swarm - pattern search optimization (PS²) algorithm with graphics hardware acceleration for bound constrained nonlinear optimization problems. The objective of this study is to determine the effectiveness of using graphics processing units (GPU) as a hardware platform for particle swarm optimization (PSO). GPU, the common graphics hardware which can be found in many personal computers, can be used for desktop data-parallel computing. The classical PSO is adapted in the data-parallel GPU computing platform featuring dasiasingle instruction - multiple threadpsila (SIMT). PSO is also enhanced by adding a local pattern search (PS) improvement. The hybrid PS² optimization method is implemented in the GPU environment and with a central processing unit (CPU) in a PC. Computational results indicate that GPU-accelerated SIMT-PS² method is orders of magnitude faster than the corresponding CPU implementation. The main contribution of this paper is the parallelization analysis and performance analysis of the hybrid PS² with GPU acceleration.