中文摘要：

采用一种微粒群优化算法来识别承压完整井非稳定地下水运动Theis公式中的水文地质参数。微粒群算法是一种新型的群体智能算法，它将每个个体看作在多维搜索空间中的一个没有重量和体积的微粒，并在搜索空间中以一定的速度飞行，该飞行速度由个体的飞行经验和群体的飞行经验进行动态调整。然后根据个体适应值大小运算，根据适应度函数对微粒的速度和位置进行进化，最终得到足够好的适应度值。本文采用微粒群算法可根据抽水试验资料快速反演Theis公式近似解析解中的水文地质参数。实例计算结果表明该微粒群算法计算速度快，在水文地质逆问题求解中值得推广应用。

英文摘要：

The particle swarm optimization （PSO） is a new intelligence algorithm, which is used to solve the approximately analytical solutions of Theis equation for the problem of the unsteady flow of fully penetrating wells in confined aquifers. In the particle swarm optimization algorithm, every individual is assumed to be the particle without weight and volume, and these particles fly within the searching space in a certain velocity. Their velocities are adjusted with both the individual＇s and the group＇s aviation experiences. The calculation is carried out according to the individual adaptive values, and the individual＇s velocities and positions are evolved. Finally the best adaptive value is obtained. In addition, using the aquifer test data to identify the hydrogeological parameters in a short time is important and meaningful to have a basic understanding for the hydrogeological conditions of the field. The case calculating results indicate that the calculation is fast, which could be used widely in the solution for hydrogeological issues.