PARALLEL BIOGEOGRAPHY-BASED OPTIMIZATION WITH GPU ACCELERATION FOR NONLINEAR OPTIMIZATION

This paper presents a massively parallel Biogeography-based Optimization Pattern Search (BBO-PS) algorithm with graphics hardware acceleration on bound constrained optimization problems. The objective of this study was to determine the effectiveness of using Graphics Processing Units (GPU) as a hardware platform for BBO-PS. GPU, the common graphics hardware found in modern personal computers (PC), can be used for data-parallel computing in a desktop setting. In this research, the BBO was adapted in the data-parallel GPU computing platform featuring 'Single Instruction Multiple Thread' (SIMT). The global optimal search of the BBO was enhanced by the classical local Pattern Search (PS) method. The hybrid BBO-PS method was implemented in the GPU environment, and compared to a similar implementation in the common computing environment with a Central Processing Unit (CPU). Computational results indicated that GPU-accelerated SIMI-BBO-PS method was orders of magnitude faster than the corresponding CPU implementation. The main contribution of this paper was the parallelization analysis and performance analysis of the hybrid BBO-PS with GPU acceleration. The research result was significant in that it demonstrated a very promising direction for high speed optimization with desktop parallel computing on a personal computer (PC).