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| Titel |
How effective and efficient are multiobjective evolutionary algorithms at hydrologic model calibration? |
| VerfasserIn |
Y. Tang, P. Reed, T. Wagener |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 10, no. 2 ; Nr. 10, no. 2 (2006-05-08), S.289-307 |
| Datensatznummer |
250007988
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| Publikation (Nr.) |
 copernicus.org/hess-10-289-2006.pdf |
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| Zusammenfassung |
| This study provides a comprehensive assessment of state-of-the-art
evolutionary multiobjective optimization (EMO) tools' relative
effectiveness in calibrating hydrologic models. The relative
computational efficiency, accuracy, and ease-of-use of the following
EMO algorithms are tested: Epsilon Dominance Nondominated Sorted
Genetic Algorithm-II (ε-NSGAII), the Multiobjective
Shuffled Complex Evolution Metropolis algorithm (MOSCEM-UA), and the
Strength Pareto Evolutionary Algorithm 2 (SPEA2). This study uses
three test cases to compare the algorithms' performances: (1) a
standardized test function suite from the computer science
literature, (2) a benchmark hydrologic calibration test case for the
Leaf River near Collins, Mississippi, and (3) a computationally
intensive integrated surface-subsurface model application in the
Shale Hills watershed in Pennsylvania. One challenge and
contribution of this work is the development of a methodology for
comprehensively comparing EMO algorithms that have different search
operators and randomization techniques. Overall, SPEA2 attained
competitive to superior results for most of the problems tested in
this study. The primary strengths of the SPEA2 algorithm lie in its
search reliability and its diversity preservation operator. The
biggest challenge in maximizing the performance of SPEA2 lies in
specifying an effective archive size without a priori knowledge of
the Pareto set. In practice, this would require significant
trial-and-error analysis, which is problematic for more complex,
computationally intensive calibration applications.
ε-NSGAII appears to be superior to MOSCEM-UA and
competitive with SPEA2 for hydrologic model calibration.
ε-NSGAII's primary strength lies in its ease-of-use due
to its dynamic population sizing and archiving which lead to rapid
convergence to very high quality solutions with minimal user input.
MOSCEM-UA is best suited for hydrologic model calibration
applications that have small parameter sets and small model
evaluation times. In general, it would be expected that MOSCEM-UA's
performance would be met or exceeded by either SPEA2 or ε-NSGAII. |
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