Application of an adaptive multi-population parallel genetic algorithm with constraints in electromagnetic tomography with incomplete projections

Abstract

Electromagnetic tomography technology (EMT) is widely used in underground energy exploration. Limited by objective conditions, the collected projection data of electromagnetic waves are sparse and incomplete. Therefore, a study of the tomographic inversion algorithm of EMT based on incomplete projection data has an important guiding significance for the exploitation of underground energy. As a global optimization probability search algorithm, the simple genetic algorithm (SGA) has been widely used in the process of tomographic inversion. However, SGA evolves through a single population, and the values of crossover and mutation probability are always fixed, so there are risks of premature convergence and poor local search ability. To improve the performance of the SGA, a new approach of adaptive multi-population parallel genetic algorithm (AMPGA) with constraints is proposed in this paper. First, the AMPGA makes full use of multi-group adaptive co-evolution to improve the local and global search ability of SGA and restrain the risk of premature convergence. Then, the introduction of prior information as a constraint makes the results clearer and more accurate. The proposed algorithm has been verified in a numerical experiment and field tests, and the results show that the proposed algorithm can well balance global and local search capabilities, which offers a more realistic and stable tomographic result.