[Objective]Parameter calibration is an important factor affecting the accuracy of hydrological prediction.The intelli-gent algorithm can effectively improve the calibration effect of hydrological model parameters.[Methods]An adaptive operator based on population dispersion is adopted to optimize the crossover,variation and migration process of genetic algorithm.The coarse-grained parallel computing model is used to improve the efficiency of population evolution.Based on the above method,a parallel genetic algorithm based on adaptive strategy is proposed.The traditional genetic algorithm(GA),serial adaptive genetic algorithm(AGA)and parallel adaptive genetic algorithm(PAGA)were respectively applied to the parameter calibration of Xin'anjiang model in Tunxi Basin.The comprehensive performance of PAGA algorithm is verified from four aspects of calibration efficiency,calibration convergence,calibration stability and calibration effect.[Results]The results show that the PAGA algo-rithm has a remarkable acceleration effect,and the calculation time is reduced by 87.9％compared with AGA algorithm in the 10-core environment.In the later stage of evolution,PAGA algorithm can converge more stably to the optimal solution,and the value of the objective function after convergence has better stability.In the verification period,the model optimized by PAGA al-gorithm has the best simulation effect,the overall pass rate of flood simulation is greater than 90％,and the average certainty co-efficient is 0.84.[Conclusion]PAGA algorithm can obviously reduce the time of parameter optimization,improve the model cal-ibration effect and convergence performance,and provide a new idea for the hydrological model parameter calibration.
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