Accelerating parallel particle swarm optimization via GPU
Journal
Optimization Methods and Software
Journal Volume
27
Journal Issue
1
Pages
33-51
Date Issued
2012
Author(s)
Hung, Y.
Abstract
Particle swarm optimization (PSO) is a population-based stochastic and derivative-free method that has been used to solve various optimization problems due to its simplicity and efficiency. While solving high-dimensional or complicated problems, PSO requires a large number of particles to explore the problem domains and consequently introduces high computational costs. In this paper, we focus on the acceleration of PSO for solving box-constrained, load-balanced optimization problems by parallelization on a graphics processing unit (GPU). We propose a GPU-accelerated PSO (GPSO) algorithm by using a thread pool model and implement GPSO on a GPU. Numerical results show that the GPU architecture fits the PSO framework well by reducing computational timing, achieving high parallel efficiency and finding better optimal solutions by using a large number of particles. For example, while solving the 100-dimensional test problems with 65,536 (162 12) particles, GPSO has achieved up to 280X and 83X speedups on a NVIDIA Tesla C1060 1.30GHz GPU relative to an Intel Xeon-X5450 3.00GHz central processing unit running in single-and quad-core mode, respectively. GPSO provides a promising method for tackling high-dimensional and difficult optimization problems using a low-cost and many-core GPU system. © 2012 Taylor and Francis Group, LLC.
SDGs
Type
journal article