关键词: 飞蛾火焰优化算法, 粒子群优化算法, 仿真验证, 含水层参数, 参数反演

Abstract: 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.

Key words: moth-flame optimization algorithm, particle swarm optimization algorithm, simulation verification, aquifer parameter, parameter inversion