Adaptive Hybrid Genetic Algorithm for Groundwater Remediation Design
Publication: Journal of Water Resources Planning and Management
Volume 131, Issue 1
Abstract
Optimal groundwater remediation design problems are often complex, nonlinear, and computationally intensive. Genetic algorithms allow solution of more complex nonlinear problems than traditional gradient-based approaches, but they are more computationally intensive. One way to improve performance is through inclusion of local search, creating a hybrid genetic algorithm (HGA). This paper presents a new self-adaptive HGA (SAHGA) and compares its performance to a nonadaptive hybrid genetic algorithm (NAHGA) and the simple genetic algorithm (SGA) on a groundwater remediation problem. Of the two hybrid algorithms, SAHGA is shown to be far more robust than NAHGA, providing fast convergence across a broad range of parameter settings. For the test problem, SAHGA needs 75% fewer function evaluations than SGA, even with an inefficient local search method. These findings demonstrate that SAHGA has substantial promise for enabling solution of larger-scale problems than was previously possible.
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Acknowledgments
This material is based upon work supported by the National Science Foundation under Grant No. BES 97-34076 CAR. The writers thank Dr. Patrick Reed for suggesting the injection solution approach tested in this work. The writers also wish to thank two anonymous reviewers and the first writer’s Ph.D. committee members, Dr. David E. Goldberg, Dr. Albert J. Valocchi, and Dr. J. Wayland Eheart, for their helpful suggestions that improved the quality of this paper. Any opinions, findings and conclusions or recommendations expressed in this material are those of the writers and do not necessarily reflect the views of the National Science Foundation (NSF).
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Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 131 • Issue 1 • January 2005
Pages: 14 - 24
Copyright
© 2005 ASCE.
History
Received: Nov 6, 2002
Accepted: May 5, 2004
Published online: Jan 1, 2005
Published in print: Jan 2005
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