Many heuristic algorithms for solving optimization problems have been proposed, and efforts have been made to improve their efficiency in finding optimal solutions. However, the sampling-based search strategies of the heuristic optimization algorithms still require considerable time to locate optima, particularly in the hyper-dimensional parameter space that are typical for distributed hydrologic models. This study evaluated the performance of parallel computing methods, 'spmd' and 'parfor,' of Matrix Laboratory (MATLAB) in improving computational efficiency of solving optimization problems using sampling-based algorithms in hydrologic and water quality modeling applications. The parallel computing methods were applied in calibrating 34 parameters for hydrologic simulation of the Soil and Water Assessment Tool (SWAT) model and in identifying watershed-scale optimum spatial distributions of corn stover removal using the AMALGAM optimization algorithm. In the applications, the SWAT model was calibrated with NSE of 0.86 and R2 of 0.93 at the watershed outlet for the calibration period of 1993 to 1999 using the parallel computing methods, and the calibrated model provided NSE of 0.84 and R2 of 0.92 for the validation period of 2000 to 2009. The results clearly demonstrated the effectiveness of the methods in reducing computational time of the parameter calibration and spatial optimization. The SWAT model code modification was not required, and changes in the source code of the original sequential optimization algorithm were minimal. Thus, the parallel computing methods of MATLAB are expected to provide simple and quick solutions for improving efficiency of solving the optimization problems in watershed model applications.
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