A Hybrid Optimization Method Based on Genetic Algorithm for Magnetotelluric Inverse Problem

Genetic algorithm, one of the new methods for global non-linear optimization problem, has been applied in magnetotelluric data analysis. In this paper, the magnetotelluric inverse problem was studied by a hybrid genetic algorithm, which was based on the combination of simplex method and genetic algorithm. The standard genetic algorithm has poor local search ability, large amounts of calculation, and adaptability to large space. On the other hand, simplex method based on local linearization is usually lost in local minimum values. So a new method was put forward through a promoted simplex operator embedded into genetic algorithm and the strategy was adopted to keep the group of best individuals. The new algorithm has not only the global of genetic algorithm, but also the fast convergence of the simplex algorithm. The inversion results of synthetic magnetotelluric data shows that the algorithm possesses advantages of expediting convergence, avoiding earliness and improving precision.