Efficient Hierarchical Parallel Genetic Algorithms using Grid computing

  • Authors:

  • Dudy Lim;Yew-Soon Ong;Yaochu Jin;Bernhard Sendhoff;Bu-Sung Lee

  • Affiliations:

  • School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, Singapore;School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, Singapore;Honda Research Institute Europe GmbH, Carl-Legien Strasse 30, 63073 Offenbach, Germany;Honda Research Institute Europe GmbH, Carl-Legien Strasse 30, 63073 Offenbach, Germany;School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, Singapore

  • Venue:
  • Future Generation Computer Systems
  • Year:
  • 2007

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Abstract

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.