Constrained shuffled complex evolution algorithm and its application in the automatic calibration of Xinanjiang model

The Shuffled Complex Evolution—University of Arizona (SCE-UA) is a classical algorithm in the field hydrology and water resources, but it cannot solve constrained optimization problems directly. Using penalty functions has been preferred method to handle constraints, appropriate selection parameters can be challenging. To enhance universality SCE-UA, we propose Constrained Evolution Algorithm (CSCE) conveniently effectively inequality-constrained problems. Its performance compared with SCE-UA using adaptive function (SCEA) on 14 test inequality constraints. It further seven other algorithms two low success rates. demonstrate its effect hydrologic model calibration, CSCE applied parameter Xinanjiang (XAJ) under synthetic data observed data. results indicate that more advantageous than SCEA terms rate, stability, feasible convergence speed. guarantee feasibility solution avoid problem deep soil tension capacity (WDM)<0 process XAJ model. In case data, accurately find theoretical optimal given optimized by simulate hourly rainfall-runoff events Hexi Basin achieves mean Nash efficiency coefficients greater 0.75 calibration period validation period.