A co-evolutionary differential evolution algorithm for solving min-max optimization problems implemented on GPU using C-CUDA

被引：29

作者：

Fabris, Fabio ^{[1
]}

Krohling, Renato A. ^{[1
]}

机构：

[1] Univ Fed Espirito Santo, Dept Comp Sci, BR-29075910 Vitoria, ES, Brazil

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2012年 / 39卷 / 12期

关键词：

Optimization; Differential evolution; Co-evolutionary algorithms; Graphics processing unit (GPU); Compute unified device architecture (CUDA); Computational performance assessment; PARTICLE SWARM OPTIMIZATION; PERFORMANCE;

D O I：

10.1016/j.eswa.2011.10.015

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

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. (C) 2011 Elsevier Ltd. All rights reserved.

引用

页码：10324 / 10333

页数：10

共 26 条

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[10] A Novel Method for Solving Min-Max Problems by Using a Modified Particle Swarm Optimization
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