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A Novel Multi-Objective Shuffled Complex Differential Evolution Algorithm with Application to Hydrological Model Parameter Optimization

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Abstract

Practice experience suggests that the traditional calibration of hydrological models with single objective cannot properly measure all of the behaviors of the hydrological system. To circumvent this problem, in recent years, a lot of studies have looked into calibration of hydrological models with multi-objective. In this paper, we propose a novel multi-objective evolution algorithm entitled multi-objective shuffled complex differential evolution (MOSCDE) algorithm, which is an extension of the famous single objective algorithm, shuffled complex evolution (SCE-UA) algorithm, to the multi-objective framework. This new proposed algorithm replaces the simplex search used in SCE-UA with the differential evolution (DE) algorithm and can more thoroughly utilize the information of the individuals in the evolutionary population and improve the search ability of the algorithm. Meanwhile, the Cauchy mutation (CM) operator is employed to prevent the algorithm from falling into the local optimal region of the feasible space. Moreover, two types of archive sets are employed to further improve the performance of the algorithm. The efficacy of the MOSCDE algorithm is first tested on five benchmark problems. After achieving satisfactory performance on the test problems, the MOSCDE is applied to multi-objective parameter optimization of a hydrological model for daily runoff forecasting. The results show that the MOSCDE algorithm can be a viable alternative for multi-objective parameter optimization of hydrological model.

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Acknowledgments

This work is supported by a grant from the State Key Program of National Natural Science of China (No. 51239004), the Special Research Foundation for the Public Welfare Industry of the Ministry of Science and Technology and the Ministry of Water Resources (No.201001080), and Specialized Research Fund for the Doctoral Program of Higher Education of China (No.20100142110012). Special thanks are given to the anonymous reviewers and editors for their constructive comments.

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Authors and Affiliations

  1. School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Jun Guo, Jianzhong Zhou, Qiang Zou & Yi Liu

  2. Hubei Key Laboratory of Digital Valley Science and Technology, Wuhan, 430074, China

    Jun Guo, Jianzhong Zhou, Qiang Zou & Yi Liu

  3. Hunan Electric Power Test & Research Institute, Changsha, 410007, China

    Jun Guo

  4. Laboratory of Numerical Modeling Technique for Water Resources, Department of Water Resources and Environment, Pearl River Water Resources Research Institute, Guangzhou, 510623, China

    Lixiang Song

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  1. Jun Guo
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Correspondence to Jianzhong Zhou.

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Guo, J., Zhou, J., Zou, Q. et al. A Novel Multi-Objective Shuffled Complex Differential Evolution Algorithm with Application to Hydrological Model Parameter Optimization. Water Resour Manage 27, 2923–2946 (2013). https://doi.org/10.1007/s11269-013-0324-1

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