Efficient Hierarchical Parallel Genetic Algorithms using Grid computing

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,...

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Bibliographic Details
Published in:	Future generation computer systems Vol. 23; no. 4; pp. 658 - 670
Main Authors:	Lim, Dudy, Ong, Yew-Soon, Jin, Yaochu, Sendhoff, Bernhard, Lee, Bu-Sung
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.05.2007
Subjects:	Grid computing Parallel Genetic Algorithms Parallel Genetic Algorithms Grid computing
ISSN:	0167-739X, 1872-7115
Online Access:	Get full text
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Summary:	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.
ISSN:	0167-739X 1872-7115
DOI:	10.1016/j.future.2006.10.008