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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。
A Particle Swarm Optimization (PSO) algorithm is used to identify the hydrogeological parameters in the Theis equation of the unsteady groundwater movement in a fully pressurized well. Particle swarm optimization is a new type of swarm intelligence that treats each individual as an unweighted and volumetric particle in a multidimensional search space and flies in a search space at a speed that is determined by the individual’s Flight experience and experience of group flight dynamics. Then according to the individual fitness value operation, according to the fitness function of the particle velocity and position of evolution, finally get a good fitness value. In this paper, the particle swarm optimization algorithm can be used to quickly invert the hydrogeological parameters of the approximate analytic solution of Theis formula according to the pumping test data. The calculation results of the examples show that the particle swarm optimization algorithm is fast in calculation and worthy of promotion and application in the hydrogeological inverse problem solving.