Groundwater Remediation Design Using Multiscale Genetic Algorithms
Publication: Journal of Water Resources Planning and Management
Volume 132, Issue 5
Abstract
Water resources optimization models often use spatial numerical models to approximate the physics of natural systems. The discretization of the numerical grids can affect their search for optimal solutions, in terms of both solution reliability and computational costs. Computational costs are particularly significant for population-based optimization techniques such as genetic algorithms (GAs), which are being applied to water resources optimization. To overcome these bottlenecks, this paper proposes multiscale strategies for GAs that evaluate designs on different spatial grids at different stages of the algorithm. The strategies are initially tested on a hypothetical groundwater remediation problem, and then the best approach is used to solve a field-scale groundwater application at the Umatilla Chemical Depot in Oregon. For the Umatilla case, the multiscale GA was able to save as much as 80% of the computational costs (relative to the GA that used only the fine grid) with no loss of accuracy, thus exhibiting significant promise for improving performance of GA-based optimization methodologies for water resources applications.
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Acknowledgments
The writers would like to express their immense gratitude to Eva Sinha and Shenquan Yan for all their effort and expertise in implementing the multiscale strategies on the Umatilla Chemical Depot application using a parallel computing system. We would also like to thank Dr. David Goldberg (General Engineering, UIUC) and Laura Albert (UIUC) for their insightful discussions and conceptual ideas related to multiscale problems within the GA. Acknowledgment is also conveyed to the funding agencies the National Science Foundation (grant number BES 99-03889) and the U. S. Army Research Office (grant numbers DAAD19-00-1-0389 and DAAD19-001-1-0025). This work was partially supported by the National Center for Supercomputing Applications (NCSA) under BCS020001N and utilized the NCSA system Copper.
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Information
Published In
Journal of Water Resources Planning and Management
Volume 132 • Issue 5 • September 2006
Pages: 341 - 350
Copyright
© 2006 ASCE.
History
Received: Jan 11, 2005
Accepted: Nov 14, 2005
Published online: Sep 1, 2006
Published in print: Sep 2006
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