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• [P190] Parallel Ant Colony with Local Pattern Search for Nonlinear Function Optimization with Graphics Hardware Acceleration
  Author(s): Weihang Zhu
  
  

  Abstract
  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.

  
   

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