A multiobjective global optimization algorithm with application to calibration of hydrologic models

Persistent Link:
http://hdl.handle.net/10150/615704
Title:
A multiobjective global optimization algorithm with application to calibration of hydrologic models
Author:
Yapo, Patrice O.; Gupta, Hoshin Vijai; Sorooshian, Soroosh
Affiliation:
Department of Hydrology & Water Resources, The University of Arizona
Publisher:
Department of Hydrology and Water Resources, University of Arizona (Tucson, AZ)
Issue Date:
1997-02
Rights:
Copyright © Arizona Board of Regents
Collection Information:
This title from the Hydrology & Water Resources Technical Reports collection is made available by the Department of Hydrology & Atmospheric Sciences and the University Libraries, University of Arizona. If you have questions about titles in this collection, please contact repository@u.library.arizona.edu.
Abstract:
This report presents a new multiple objective optimization algorithm that is capable of solving for the entire Pareto set in one single optimization run. The multi-objective complex evolution (MOCOM-UA) procedure is based on the following three concepts: (1) population, (2) rank-based selection, and (3) competitive evolution. In the MOCOM-UA algorithm, a population of candidate solutions is evolved in the feasible space to search for the Pareto set. Ranking of the population is accomplished through Pareto ranking, where all points are successively placed on different Pareto fronts. Competitive evolution consists of selecting subsets of points (including all worst points in the population) based on their ranks and moving the worst points toward the Pareto set using the newly developed multi-objective simplex (MOSIM) procedure. Test analysis on the MOCOM-UA algorithm is accomplished on mathematical problems of increasing complexity and based on a bi-criterion measure of performance. The two performance criteria are: (1) efficiency, as measured by the ability of the algorithm to converge quickly, and (2) effectiveness, as measured by the ability of the algorithm to locate the Pareto set. Comparison of the MOCOM-UA algorithm against three multi-objective genetic algorithms (MOGAs) favors the former. In a realistic application, the MOCOM-UA algorithm is used to calibrate the Soil Moisture Accounting model of the National Weather Service River Forecasting Systems (NWSRFS-SMA). Multi-objective calibration of this model is accomplished using two bi-criterion objective functions, namely the Daily Root Mean Square-Heteroscedastic Maximum Likelihood Estimator (DRMS-HMLE) and rising limb /falling limb (RISE/FALL) objective functions. These two multi-objective calibrations provide some interesting insights into the influence of different objectives in the location of final parameter values, as well as limitations in the structure of the NWSRFS-SMA model.
Note:
Kimberly - catalogs do not provide keywords
Series/Report no.:
Technical Reports on Hydrology and Water Resources, No. 97-050
Sponsors:
This research was partially supported by grants from the National Science Foundation (grants EAR-9415347 and EAR-9418147), the Hydrologic Research Laboratory of the U.S. National Weather Service (grants NA37WH0385, NA47WH0408 and NA57WH0575), and by the National Aeronautics and Space Administration (NASA-EOS grant NAGW2425). Financial assistance was also provided to Dr. Patrice Yapo by The University of Arizona Graduate College. These sources of assistance are gratefully acknowledged.

Full metadata record

DC FieldValue Language
dc.contributor.authorYapo, Patrice O.en
dc.contributor.authorGupta, Hoshin Vijaien
dc.contributor.authorSorooshian, Sorooshen
dc.date.accessioned2016-07-07T18:46:49Z-
dc.date.available2016-07-07T18:46:49Z-
dc.date.issued1997-02-
dc.identifier.urihttp://hdl.handle.net/10150/615704-
dc.description.abstractThis report presents a new multiple objective optimization algorithm that is capable of solving for the entire Pareto set in one single optimization run. The multi-objective complex evolution (MOCOM-UA) procedure is based on the following three concepts: (1) population, (2) rank-based selection, and (3) competitive evolution. In the MOCOM-UA algorithm, a population of candidate solutions is evolved in the feasible space to search for the Pareto set. Ranking of the population is accomplished through Pareto ranking, where all points are successively placed on different Pareto fronts. Competitive evolution consists of selecting subsets of points (including all worst points in the population) based on their ranks and moving the worst points toward the Pareto set using the newly developed multi-objective simplex (MOSIM) procedure. Test analysis on the MOCOM-UA algorithm is accomplished on mathematical problems of increasing complexity and based on a bi-criterion measure of performance. The two performance criteria are: (1) efficiency, as measured by the ability of the algorithm to converge quickly, and (2) effectiveness, as measured by the ability of the algorithm to locate the Pareto set. Comparison of the MOCOM-UA algorithm against three multi-objective genetic algorithms (MOGAs) favors the former. In a realistic application, the MOCOM-UA algorithm is used to calibrate the Soil Moisture Accounting model of the National Weather Service River Forecasting Systems (NWSRFS-SMA). Multi-objective calibration of this model is accomplished using two bi-criterion objective functions, namely the Daily Root Mean Square-Heteroscedastic Maximum Likelihood Estimator (DRMS-HMLE) and rising limb /falling limb (RISE/FALL) objective functions. These two multi-objective calibrations provide some interesting insights into the influence of different objectives in the location of final parameter values, as well as limitations in the structure of the NWSRFS-SMA model.en
dc.description.sponsorshipThis research was partially supported by grants from the National Science Foundation (grants EAR-9415347 and EAR-9418147), the Hydrologic Research Laboratory of the U.S. National Weather Service (grants NA37WH0385, NA47WH0408 and NA57WH0575), and by the National Aeronautics and Space Administration (NASA-EOS grant NAGW2425). Financial assistance was also provided to Dr. Patrice Yapo by The University of Arizona Graduate College. These sources of assistance are gratefully acknowledged.en
dc.language.isoen_USen
dc.publisherDepartment of Hydrology and Water Resources, University of Arizona (Tucson, AZ)en
dc.relation.ispartofseriesTechnical Reports on Hydrology and Water Resources, No. 97-050en
dc.rightsCopyright © Arizona Board of Regentsen
dc.sourceProvided by the Department of Hydrology and Water Resources.en
dc.titleA multiobjective global optimization algorithm with application to calibration of hydrologic modelsen_US
dc.typetexten
dc.typeTechnical Reporten
dc.contributor.departmentDepartment of Hydrology & Water Resources, The University of Arizonaen
dc.description.noteKimberly - catalogs do not provide keywordsen
dc.description.collectioninformationThis title from the Hydrology & Water Resources Technical Reports collection is made available by the Department of Hydrology & Atmospheric Sciences and the University Libraries, University of Arizona. If you have questions about titles in this collection, please contact repository@u.library.arizona.edu.en
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