Background:
Recently, particle swarm optimization (PSO) has been increasingly used in geophysics due to its simple operation and fast convergence.
Objective:
However, PSO lacks population diversity and may fall to local optima. Hence, an improved hybrid particle swarm optimizer with sine-cosine acceleration coefficients (IH-PSO-SCAC) is proposed and successfully applied to test functions and in transient electromagnetic (TEM) nonlinear inversion.
Method:
A reverse learning strategy is applied to optimize population initialization. The sine-cosine acceleration coefficients are utilized for global convergence. Sine mapping is adopted to enhance population diversity during the search process. In addition, the mutation method is used to reduce the probability of premature convergence.
Results:
The application of IH-PSO-SCAC in the test functions and several simple layered models are demonstrated with satisfactory results in terms of data fit. Two inversions have been carried out to test our algorithm. The first model contains an underground low-resistivity anomaly body and the second model utilized measured data from a profile of the Xishan landslide in Sichuan Province. In both cases, resistivity profiles are obtained, and the inverse problem is solved for verification.
Conclusion:
The results show that the IH-PSO-SCAC algorithm is practical, can be effectively applied in TEM inversion and is superior to other representative algorithms in terms of stability and accuracy.
An Accelerated Nonlinear Contrast Source Inversion Scheme for Sparse Electromagnetic Imaging