Abstract: The performance of groundwater modeling relies largely on the accurate estmiation of hydrogeological parameters,which are often influenced by the choice of optimization methods used.The particle swarm optimization(PSO)is a swarm in telligence technique for global optimization.However,the search efficiency of PSO decreases as the iteration process approaching to the end.It is thus desirable to search for the enhanced version of PSO.In this study,a hybrid global optimization algorithm that uses the Hooke-Jeeves(HJ)method for the bcal optimization and PSO for the global optimization is proposed to address the inverse problems in ground water modeling.Hydrogeological parameters can be determined using the new HJPSO algorithm.The result of a case study shows that HJPSO is characterized as an algorithm with accuracy,fast convergence and high robustness in the estmiation of hydrogeological parameters,and applicable to hydrogeologic parameters identification.