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The application of particle swarm optimization algorithms to estimation of aquifer parameters from data of pumping test

机译:粒子群算法在抽水试验数据估算含水层参数中的应用

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With the application of article swarm optimization algorithms (PSO), the function optimization problem of analyzing pumping test data in aquifer to estimate such parameters as transmissivity and storage coefficient was to be solved. With the different number of particles and the initial guessed values of transmissivity, the numerical experiments were conducted to explore the effect of these factors on the convergence of PSO algorithm . The results show : 1) that PSO algorithm may be effectively applied to solve the function optimization problem of analyzing pumping test data in aquifer to estimate transmissivity and storage coefficient , 2..that the convergence of PSO algorithm and the computation time are influenced by the number of particles, and the fewer iterations are needed in computation with the larger number of particles , 3) that the ranges of initial guessed values of transmissivity may also bring some effect on the convergence of PSO algorithm and the computation time, the larger the ranges are , the morenumber of iterations and the longer computation time are needed to guaranteed the convergence of PSO algorithm. Compared with other current methods, PSO method is of such advantages as that the principle is easy to understand and the procedure of computation is simple to realized.
机译:结合文章群优化算法(PSO)的应用,解决了分析含水层中抽水试验数据以估算透射率和储水系数等参数的功能优化问题。在不同数量的粒子和透射率的初始猜测值的情况下,进行了数值实验,以探讨这些因素对PSO算法收敛的影响。结果表明:1)可以有效地应用PSO算法解决含水层中抽水试验数据的分析功能优化问题,从而估算透光率和储层系数; 2。粒子数,并且计算中需要的迭代次数越少,粒子数越多,3)透射率的初始猜测值范围也可能会对PSO算法的收敛性和计算时间产生影响,范围越大是,需要更多的迭代次数和更长的计算时间来保证PSO算法的收敛性。与现有的其他方法相比,PSO方法具有原理简单易懂,计算过程易于实现的优点。

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