Abstract
The heuristic algorithm represented by particle swarm optimization (PSO) is an effective tool for addressing serious nonlinearity in one-dimensional magnetotelluric (MT) inversions. PSO has the shortcomings of insufficient population diversity and a lack of coordination between individual cognition and social cognition in the process of optimization. Based on PSO, we propose a new memetic strategy, which firstly selectively enhances the diversity of the population in evolutionary iterations through reverse learning and gene mutation mechanisms. Then, dynamic inertia weights and cognitive attraction coefficients are designed through sine-cosine mapping to balance individual cognition and social cognition in the optimization process and to integrate previous experience into the evolutionary process. This improves convergence and the ability to escape from local extremes in the optimization process. The memetic strategy passes the noise resistance test and an actual MT data test. The results show that the memetic strategy increases the convergence speed in the PSO optimization process, and the inversion accuracy is also greatly improved.
Highlights
The magnetotelluric (MT) technology is a geophysical electromagnetic detection method that uses electromagnetic induction signals to detect underground electrical structures [1,2]
The electrical structure is used as the optimization parameter to find the smallest objective function, and the difference between the predicted electromagnetic signal and the observed signal is evaluated by the objective function [5]
Among the results for the four observation stations, our memetic strategy prediction results are significantly better than the traditional particle swarm optimization (PSO) prediction results
Summary
The magnetotelluric (MT) technology is a geophysical electromagnetic detection method that uses electromagnetic induction signals to detect underground electrical structures [1,2]. Several common algorithms, including the simulated annealing method, the Bayesian inversion method and genetic algorithm, have been able to initially solve the MT inversion problem and determine the underground electrical structure through the electromagnetic response signal of the MT method [12,13] Among these heuristic swarm intelligence algorithms, the particle swarm optimization (PSO) algorithm is widely used in the MT inversion due to its simple implementation and less adjustment parameters [14,15]. With the introduction of the inertia weight factor, the time-varying acceleration factor strategy and the strategy based on reproduction and subgroup hybridization, the shortcomings of PSO—that it falls into local extremes and has slow convergence in the later stages of evolution—are gradually improved [16,17,18] These algorithms still have not overcome the shortcomings of the lack of population diversity and the uncoordination of individual cognition and social cognition capabilities.
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