Shuffled Complex Evolution model calibrating algorithm: enhancing its robustness and efficiency


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Abstract

Shuffled Complex Evolution—University of Arizona (SCE-UA) has been used extensively and proved to be a robust and efficient global optimization method for the calibration of conceptual models. In this paper, two enhancements to the SCE-UA algorithm are proposed, one to improve its exploration and another to improve its exploitation of the search space. A strategically located initial population is used to improve the exploration capability and a modification to the downhill simplex search method enhances its exploitation capability. This enhanced version of SCE-UA is tested, first on a suite of test functions and then on a conceptual rainfall-runoff model using synthetically generated runoff values. It is observed that the strategically located initial population drastically reduces the number of failures and the modified simplex search also leads to a significant reduction in the number of function evaluations to reach the global optimum, when compared with the original SCE-UA. Thus, the two enhancements significantly improve the robustness and efficiency of the SCE-UA model calibrating algorithm.

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