Efficient hierarchical parallel genetic algorithms using grid computing
School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, Singapore
Future Generation Computer Systems, Volume 23, Issue 4, May 2007, Pages 658-670
@article{lim2007efficient,
title={Efficient hierarchical parallel genetic algorithms using grid computing},
author={Lim, D. and Ong, Y.S. and Jin, Y. and Sendhoff, B. and Lee, B.S.},
journal={Future Generation Computer Systems},
volume={23},
number={4},
pages={658–670},
issn={0167-739X},
year={2007},
publisher={Elsevier}
}
In this paper, we present an efficient Hierarchical Parallel Genetic Algorithm framework using Grid computing (GE-HPGA). The framework is developed using standard Grid technologies, and has two distinctive features: (1) an extended GridRPC API to conceal the high complexity of the Grid environment, and (2) a metascheduler for seamless resource discovery and selection. To assess the practicality of the framework, a theoretical analysis of the possible speed-up offered is presented. An empirical study on GE-HPGA using a benchmark problem and a realistic aerodynamic airfoil shape optimization problem for diverse Grid environments having different communication protocols, cluster sizes, processing nodes, at geographically disparate locations also indicates that the proposed GE-HPGA using Grid computing offers a credible framework for providing a significant speed-up to evolutionary design optimization in science and engineering.
February 27, 2011 by hgpu
