通过10个典型低维函数对一种新型群体智能仿生算法——飞蛾火焰优化(MFO)算法进行仿真验证,并与粒子群优化(PSO)算法的寻优结果进行对比。以无界井流问题及直线隔水边界附近井流问题的解析解为基础,将MFO算法应用于分析抽水试验数据,进行反演承压含水层参数,并以2个实例对MFO算法进行验证。结果表明:MFO算法在低维函数极值寻优问题上具有较好的收敛精度和全局寻优能力,寻优精度较PSO算法提高了7个数量级以上。MFO算法对2个实例的反演精度较文献改进SA算法等提高了56.5%以上,具有较好的稳健性能、收敛速度和全局寻优能力。利用MFO算法对承压含水层参数进行反演,可获得比相关文献更高的反演精度,不但为精确估计承压含水层参数提供了有效方法,而且拓展了MFO算法在地下水模型参数反演中的应用,具有良好的应用价值和前景。

Through 10 typical low dimensional functions, we validate a new kind of swarm intelligent bionic algorithm by simulation, namely moth-flame optimization (MFO)algorithm. We compare the optimization results between MFO algorithm and particle swarm optimization (PSO) algorithm. On the basis of analytical solution of well flow problem without boundary and water flow problem near straight boundary for water insulation, we apply MFO algorithm to analyzing pumping test data and carry out parameter inversion of confined aquifer. Two examples are used to verify MFO algorithm.Results show that, 1) MFO algorithm has advantages such as high convergence accuracy and good global optimization ability in the optimization problem for low dimensional function extremum, which is superior to PSO algorithm, and optimization accuracy of MFO algorithm is higher than that of PSO algorithm by 7 orders of magnitude; 2) MFO algorithm has good robustness, fast convergence speed and global optimization ability, exceeding improved SA algorithm by 56.5% in inversion accuracy for the 2 examples; 3) by using MFO algorithm, we can have an effective method to estimate the parameters of confined aquifer, and also effectively conduct parameter inversion for underground water model. Finally, in comparison with methods in relevant literatures, MFO algorithm has better inversion accuracy and good application value.