Parallel ant colony for nonlinear function optimization with graphics hardware acceleration

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.