### Abstract

Practical experience with the calibration of hydrologic models suggests that any single-objective function, no matter how carefully chosen, is often inadequate to properly measure all of the characteristics of the observed data deemed to be important. One strategy to circumvent this problem is to define several optimization criteria (objective functions) that measure different (complementary) aspects of the system behavior and to use multicriteria optimization to identify the set of nondominated, efficient, or Pareto optimal solutions. In this paper, we present an efficient and effective Markov Chain Monte Carlo sampler, entitled the Multiobjective Shuffled Complex Evolution Metropolis (MOSCEM) algorithm, which is capable of solving the multiobjective optimization problem for hydrologic models. MOSCEM is an improvement over the Shuffled Complex Evolution Metropolis (SCEM-UA) global optimization algorithm, using the concept of Pareto dominance (rather than direct single-objective function evaluation) to evolve the initial population of points toward a set of solutions stemming from a stable distribution (Pareto set). The efficacy of the MOSCEM-UA algorithm is compared with the original MOCOM-UA algorithm for three hydrologic modeling case studies of increasing complexity.

Original language | English (US) |
---|---|

Journal | Water Resources Research |

Volume | 39 |

Issue number | 8 |

State | Published - Aug 2003 |

### Fingerprint

### Keywords

- Hydrologic models
- Markov chain Monte Carlo
- Multicriteria calibration
- Parameter optimization
- Pareto ranking
- Population diversity

### ASJC Scopus subject areas

- Environmental Science(all)
- Environmental Chemistry
- Aquatic Science
- Water Science and Technology

### Cite this

*Water Resources Research*,

*39*(8).

**Effective and efficient algorithm for multiobjective optimization of hydrologic models.** / Vrugt, Jasper A.; Gupta, Hoshin Vijai; Bastidas, Luis A.; Bouten, Willem; Sorooshian, Soroosh.

Research output: Contribution to journal › Article

*Water Resources Research*, vol. 39, no. 8.

}

TY - JOUR

T1 - Effective and efficient algorithm for multiobjective optimization of hydrologic models

AU - Vrugt, Jasper A.

AU - Gupta, Hoshin Vijai

AU - Bastidas, Luis A.

AU - Bouten, Willem

AU - Sorooshian, Soroosh

PY - 2003/8

Y1 - 2003/8

N2 - Practical experience with the calibration of hydrologic models suggests that any single-objective function, no matter how carefully chosen, is often inadequate to properly measure all of the characteristics of the observed data deemed to be important. One strategy to circumvent this problem is to define several optimization criteria (objective functions) that measure different (complementary) aspects of the system behavior and to use multicriteria optimization to identify the set of nondominated, efficient, or Pareto optimal solutions. In this paper, we present an efficient and effective Markov Chain Monte Carlo sampler, entitled the Multiobjective Shuffled Complex Evolution Metropolis (MOSCEM) algorithm, which is capable of solving the multiobjective optimization problem for hydrologic models. MOSCEM is an improvement over the Shuffled Complex Evolution Metropolis (SCEM-UA) global optimization algorithm, using the concept of Pareto dominance (rather than direct single-objective function evaluation) to evolve the initial population of points toward a set of solutions stemming from a stable distribution (Pareto set). The efficacy of the MOSCEM-UA algorithm is compared with the original MOCOM-UA algorithm for three hydrologic modeling case studies of increasing complexity.

AB - Practical experience with the calibration of hydrologic models suggests that any single-objective function, no matter how carefully chosen, is often inadequate to properly measure all of the characteristics of the observed data deemed to be important. One strategy to circumvent this problem is to define several optimization criteria (objective functions) that measure different (complementary) aspects of the system behavior and to use multicriteria optimization to identify the set of nondominated, efficient, or Pareto optimal solutions. In this paper, we present an efficient and effective Markov Chain Monte Carlo sampler, entitled the Multiobjective Shuffled Complex Evolution Metropolis (MOSCEM) algorithm, which is capable of solving the multiobjective optimization problem for hydrologic models. MOSCEM is an improvement over the Shuffled Complex Evolution Metropolis (SCEM-UA) global optimization algorithm, using the concept of Pareto dominance (rather than direct single-objective function evaluation) to evolve the initial population of points toward a set of solutions stemming from a stable distribution (Pareto set). The efficacy of the MOSCEM-UA algorithm is compared with the original MOCOM-UA algorithm for three hydrologic modeling case studies of increasing complexity.

KW - Hydrologic models

KW - Markov chain Monte Carlo

KW - Multicriteria calibration

KW - Parameter optimization

KW - Pareto ranking

KW - Population diversity

UR - http://www.scopus.com/inward/record.url?scp=1542442237&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=1542442237&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:1542442237

VL - 39

JO - Water Resources Research

JF - Water Resources Research

SN - 0043-1397

IS - 8

ER -