Global optimization algorithms for particle swarm optimization to the derivation of horizontal multilayered soil models considering the frequency dependence of soil parameters

Abstract

The frequency dependence of soil parameters has been confirmed by many experiments. Most of them are done by the measurement of soil sample in laboratories. Although some studies are based on field measurement, only homogeneous ground or two‐layer model are considered. The study of frequency dependence of soil parameters with considering multilayered model is lacking. The frequency dependence of the horizontally multilayered soil model is studied by the inversion of soil parameters in the frequency domain. The inversion of frequency domain soil parameters is translated into an optimization problem. The model parameters are determined by spectral induced polarization data, and particle swarm optimization is applied to optimizing model parameters. The performances of three different methods to trap particles inside boundaries are compared. In order to improve the computational efficiency of the inversion program, parallel computing is combined with particle swarm optimization to solve the optimization problem. The execution time of the developed algorithm before and after the application of parallel computing is compared. The performances of other two optimization algorithms, simulated annealing and genetic algorithm, are compared with that of particle swarm optimization. So it would be more clear which optimization algorithm should be selected among these three commonly used algorithms when dealing with the inversion problem of soil parameters. The accuracy of the proposed method is verified by a numerical simulation experiment. Then, this method is applied to interpreting field data, and the frequency dependence of soil parameters can be observed from the inversion results.

Inversion of one‐dimensional parameters of horizontal multi‐layer soil model based on dynamic state electromagnetic field theory and ant colony optimization algorithm

“…In order to show the performance of ant colony optimization algorithm in soil parameter inversion, we consider comparing it with other global optimization algorithms such as simulated annealing (SA), genetic algorithm (GA), and particle swarm optimization (PSO) which have been applied to soil inversion in other literatures. [11][12][13] From the perspective of global search capability and convergence efficiency, the comparison results are shown in Table 7. It can be clearly seen that ant colony optimization algorithm greatly improves the computational efficiency while ensuring to find the global optimal solution.…”

“…Reference 12 compares genetic algorithm with simulated annealing in terms of parameter convergence. In Reference 13, the actual two‐layer model measurement data is used to verify the effectiveness of the particle swarm optimization algorithm. In order to better explore the performance of global algorithms in soil parameter inversion, and considering the diversity of global algorithms, it is necessary to explore the applicability of ant colony optimization algorithm in soil parameter inversion.…”