改进粒子群算法在确定含水层参数中的应用

通过分析抽水试验数据,为估计含水层参数提供新的方法。在粒子多样性方面对粒子群算法进行改进,提高了算法的收敛速度和精度。将改进的粒子群优化算法应用到含水层参数估计中,计算结果与其他方法进行对比,并对不同初始值范围下参数估计值进行分析探讨。结果表明:改进粒子群算法估计结果相对误差(7.3%和4.5%)小于其他方法,且目标函数值相对更小,达到0.335×10~(-5);对于不同初始参数范围,利用此算法均能达到满意结果且寻优率高。基于抽水试验数据估计含水层参数的改进粒子群优化算法计算结果有效且可靠,算法收敛速度快,寻优能力强,稳定性好。

Applications of the improved particle swarm algorithm to estimate aquifer parameters

To analyze pumping test data can supply a new method for estimating aquifer parameters. The particle swarm algorithm improved in terms of particle diversity increased convergence speed and accuracy of the algorithm. The improved particle swarm optimization algorithm was applied to estimate the aquifer parameters. The calculated results were compared with the other methods , and the estimated values of parameters under different initial scopes were analyzed and discussed. The results showed that, the relative errors of improved particle swarm algorithm (which were 7.3% and 4.5%, respectively) were smaller than the relative errors of the other methods, and the objective function value was also relatively smaller, reaching 0.335×10^-5; For different initial parameter ranges, the improved algorithm obatined a satisfactory parameter estimation result and maintained a high rate of optimization search. Based on pumping test data, the results of improved particle swarm optimization algorithm for estimating aquifer parameters were more effective and reliable with fast convergence speed, strong optimization ability and good stability.