Particle Swarm with graphics hardware acceleration and local pattern search on bound constrained problems
Weihang Zhu, James R. Curry · 2009
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 PS2optimization method is implemented in the GPU environment and with a central processing unit (CPU) in a PC. Computational results indicate that GPU-accelerated SIMT-PS2method 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 PS2with GPU acceleration.