Shuffled Complex Evolution model calibrating algorithm: enhancing its robustness and efficiency |
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| Authors: | Nitin Muttil A W Jayawardena |
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| Institution: | 1. School of Architectural, Civil and Mechanical Engineering and Institute for Sustainability and Innovation, Victoria University, PO Box 14428, Melbourne, VIC, Australia, 8001;2. International Centre for Water Hazard and Risk Management, Public Works Research Institute, 1‐6, Minamihara, Tsukuba, Ibaraki 305‐8516, Japan (Formally from the Department of Civil Engineering, The University of Hong Kong) |
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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. Copyright © 2008 John Wiley & Sons, Ltd. |
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| Keywords: | evolutionary computation optimization calibration hydrologic models |
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