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

• Published in: 2009 IEEE Swarm Intelligence Symposium pp. 1 - 8
• Main Authors: Weihang Zhu, Curry, J.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.03.2009
• Subjects: Acceleration; Central Processing Unit; Computer aided instruction; Computer graphics; Constraint optimization; Hardware; Microcomputers; Optimization methods; Particle swarm optimization; Performance analysis
• ISBN: 1424427622; 9781424427628
• DOI: 10.1109/SIS.2009.4937837

This paper presents a particle swarm - pattern search optimization (PS 2 ) 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 PS 2 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-PS 2 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 PS 2 with GPU acceleration.