Parallel ant colony for nonlinear function optimization with graphics hardware acceleration
Dept. of Ind. Eng., Lamar Univ., Beaumont, TX, USA
IEEE International Conference on Systems, Man and Cybernetics, 2009. SMC 2009
@inproceedings{zhu2009parallel,
title={Parallel ant colony for nonlinear function optimization with graphics hardware acceleration},
author={Zhu, W. and Curry, J.},
booktitle={Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on},
pages={1803–1808},
year={2009},
organization={IEEE}
}
This paper presents a massively parallel ant colony optimization – pattern search (ACO-PS) algorithm with graphics hardware acceleration on nonlinear function optimization problems. The objective of this study is to determine the effectiveness of using graphics processing units (GPU) as a hardware platform for ACO-PS. GPU, the common graphics hardware found in modern personal computers, can be used for data-parallel computing in a desktop setting. In this research, the classical ACO is adapted in the data-parallel GPU computing platform featuring ‘single instruction – multiple thread’ (SIMT). The global optimal search of the ACO is enhanced by the classical local pattern search (PS) method. The hybrid ACO-PS method is implemented in a GPU + CPU hardware platform and compared to a similar implementation in a central processing unit (CPU) platform. Computational results indicate that GPU-accelerated SIMT-ACO-PS 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 ACO-PS with GPU acceleration.
May 30, 2011 by hgpu
