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Application research of multi-objective Artificial Bee Colony optimization algorithm for parameters calibration of hydrological model

  • S.I. : Emergence in Human-like Intelligence towards Cyber-Physical Systems
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Abstract

Parameter optimization methods for hydrological models have an important impact for the hydrological forecasting. To achieve the parameters’ optimization and calibration for the distributed, conceptual watershed Xinanjiang model effectively and accurately, a multi-objective Artificial Bee Colony algorithm named RMOABC which adopts the mechanisms of regulation operator and Adaptive Grid is introduced in the paper. In the evolution of the algorithm, the regulation operator mechanism can balance the weights of local search and global search, and the Adaptive Grid mechanism is utilized to evaluate and maintain the Pareto solutions in the external archive. In the experiments, three commonly used multi-objective optimization algorithms, the NSGA-II, the ε-MOEA and the SMPSO, with the RMOABC algorithm were applied in Heihe River Basin, and the parameter optimization problem of Xinanjiang hydrological model was taken as the application case for long-term runoff prediction to validate and compare their performance. The experiments results showed the RMOABC algorithm can provide more comprehensive and reliable parameters sets for practical hydrological forecasting in the study area with lower execution time.

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Acknowledgments

This work is supported by National Nature Science Foundation of China (Grant Nos. 61462058, 61741201) and Gansu Province Science and Technology Program (Grant No. 1606RJZA004).

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  1. School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, People’s Republic of China

    Jiuyuan Huo

  2. College of Information Science and Technology, Gansu Agricultural University, Lanzhou, 730070, People’s Republic of China

    Liqun Liu

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Correspondence to Jiuyuan Huo.

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Huo, J., Liu, L. Application research of multi-objective Artificial Bee Colony optimization algorithm for parameters calibration of hydrological model. Neural Comput & Applic 31, 4715–4732 (2019). https://doi.org/10.1007/s00521-018-3483-4

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