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

Persistent Link:
http://hdl.handle.net/10150/290649
Title:
A multiobjective global optimization algorithm with application to calibration of hydrologic models
Author:
Yapo, Patrice Ogou, 1967-
Issue Date:
1996
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
This dissertation 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 used 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.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Hydrology.; Engineering, Industrial.; Operations Research.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Systems and Industrial Engineering
Degree Grantor:
University of Arizona
Advisor:
Sorooshian, Soroosh

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleA multiobjective global optimization algorithm with application to calibration of hydrologic modelsen_US
dc.creatorYapo, Patrice Ogou, 1967-en_US
dc.contributor.authorYapo, Patrice Ogou, 1967-en_US
dc.date.issued1996en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractThis dissertation 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 used 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_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectHydrology.en_US
dc.subjectEngineering, Industrial.en_US
dc.subjectOperations Research.en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineSystems and Industrial Engineeringen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorSorooshian, Sorooshen_US
dc.identifier.proquest9720569en_US
dc.identifier.bibrecord.b34504321en_US
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