A co-evolutionary differential evolution algorithm for solving min-max optimization problems implemented on GPU using C-CUDA
- 作者：Fá ; bio Fabris ; ffabris@inf.ufes.br ; Renato A. Krohling ; krohling.renato@gmail.com
- 关键词：Optimization ; Differential evolution ; Co-evolutionary algorithms ; Graphics processing unit (GPU) ; Compute unified device architecture (CUDA) ; Computational performance assessment
- 刊名：Expert Systems with Applications
- 出版年：2012
- 期刊代码：62_09574174
- 类别：et
- 出版时间：15 September, 2012
- 卷：39
- 期：12
- 页码：10324-10333
- 文件大小：1437 K
摘要
Several areas of knowledge are being benefited with the reduction of the computing time by using the technology of graphics processing units (GPU) and the compute unified device architecture (CUDA) platform. In case of evolutionary algorithms, which are inherently parallel, this technology may be advantageous for running experiments demanding high computing time. In this paper, we provide an implementation of a co-evolutionary differential evolution (DE) algorithm in C-CUDA for solving min-max problems. The algorithm was tested on a suite of well-known benchmark optimization problems and the computing time has been compared with the same algorithm implemented in C. Results demonstrate that the computing time can significantly be reduced and scalability is improved using C-CUDA. As far as we know, this is the first implementation of a co-evolutionary DE algorithm in C-CUDA.