Nonlinear optimization with a massively parallel Evolution Strategy–Pattern Search algorithm on graphics hardware

Abstract

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).