How effective and efficient are multiobjective evolutionary algorithms at hydrologic model calibration?

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 AlgorithmII (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. εNSGAII’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 Correspondence to: P. Reed ([email protected]) 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.