Memetic Strategy of Particle Swarm Optimization for One-Dimensional Magnetotelluric Inversions

Ruiheng Li, Lei Gao, Nian Yu, Jianhua Li, Yang Liu, Enci Wang and Xiao Feng
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Ruiheng Li: School of Electrical Engineering, Chongqing University, Chongqing 400044, China
Lei Gao: School of Electrical Engineering, Chongqing University, Chongqing 400044, China
Nian Yu: School of Electrical Engineering, Chongqing University, Chongqing 400044, China
Jianhua Li: Key Laboratory of Geophysical Electromagnetic Probing Technologies of Ministry of Natural Resources, Institute of Geophysical and Geochemical Exploration, Chinese Academy of Geological Science, Langfang 065000, China
Yang Liu: School of Electrical Engineering, Chongqing University, Chongqing 400044, China
Enci Wang: School of Electrical Engineering, Chongqing University, Chongqing 400044, China
Xiao Feng: School of Economics and Business Administration, Chongqing University, Chongqing 400044, China

Mathematics, 2021, vol. 9, issue 5, 1-22

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.

Keywords: particle swarm optimization; magnetotelluric; one-dimensional inversions; geoelectric model; optimization problem (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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