Standard Interactive Genetic Algorithm—Comprehensive Optimization Framework for Groundwater Monitoring Design
Publication: Journal of Water Resources Planning and Management
Volume 134, Issue 6
Abstract
Optimization for water resources management typically requires many simplifying assumptions about the definition and characteristics of the policy or design application in order to express decision makers’ criteria as mathematical objectives and constraints. However, real-world applications often involve important subjective information that cannot be reflected in mathematical expressions accurately or completely. This can result in mathematically optimized solutions that are less meaningful or desirable to decision makers. To address this issue, this paper presents the standard interactive genetic algorithm (SIGA) methodology that enables human decision makers to effectively analyze subjective information that is not easily quantifiable and make decisions about the quality of a design based on their preferences. These decisions are used as continuous run-time subjective feedback, along with the mathematically defined objectives and constraints, to search for optimal designs that reflect both quantitative and qualitative objectives. Although this interactive optimization methodology is applicable for any water resources planning and management problems, this paper focuses on exploring the benefits of such an approach within the domain of groundwater monitoring design. Systematic procedures and guidelines for designing a SIGA are presented, along with proposed strategies for improving the performance of SIGA. The SIGA approach is also compared with a noninteractive genetic algorithm strategy for a real-world application, and the advantages and limitations of the interactive strategy are examined.
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Acknowledgments
The writers would like to thank Dr. Takagi (Kyushu University, Japan) for his insightful discussions on this subject, and Dennis Beckmann (BP Corporation North America) for providing them with the case study. This research was supported by the Department of Energy under Grant No. DOEDE-FG07-02ER635302 and Office of Naval Research Grant No. ONRN00014-04-1-0437.
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Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 134 • Issue 6 • November 2008
Pages: 538 - 547
Copyright
© 2008 ASCE.
History
Received: Mar 12, 2007
Accepted: Feb 22, 2008
Published online: Nov 1, 2008
Published in print: Nov 2008
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