Nonlinear optimization with a massively parallel Evolution Strategy-Pattern Search algorithm on graphics hardware
This paper presents a massively parallel Evolution Strategy-Pattern Search Optimization (ES-PS) algorithm with graphics hardware acceleration on bound constrained nonlinear continuous optimization problems. The algorithm was specifically designed for a graphic processing unit (GPU) hardware platform featuring 'Single Instruction Multiple Thread' (SIMT). Evolution Strategy is a population-based evolutionary algorithm for solving complex optimization problems. GPU computing is an emerging desktop parallel computing platform. The hybrid ES-PS optimization method was implemented in the GPU environment and compared to a similar implementation on Central Processing Units (CPU). Computational results indicated that GPU-accelerated SIMT-ES-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 ES-PS with GPU acceleration. The computational results demonstrated a promising direction for high speed optimization with desktop parallel computing on a personal computer (PC). (C) 2010 Elsevier B.V. All rights reserved.
gpu evolution strategy evolutionary computations pattern search