A Proportional Genetic Algorithm for Image Reconstruction of Static Electrical Impedance Tomography
Yijia Zhang, Huaijin Chen, Lu Yang, Kai Liu, Fang Li, Chen Bai, Hongtao Wu, Jiafeng Yao
Abstract
A proportional Genetic Algorithm (pGA) is proposed to solve the inverse problem for the image reconstruction of static electrical impedance tomography (EIT). The p GA obtains static EIT image with higher convergence speed and better reconstruction image quality by combining Genetic Algorithm (GA) with a ratio objective function. Although GA gets better performance for EIT inverse problem than any other traditional methods like Tikhonov regularization, it is high time complex and slow convergence. In this paper, a ratio objective function is proposed to solve the problem. Firstly, two kinds of methods: the Tikhonov regularization algorithm and the p GA are used for image reconstruction in the simulation. The image quality of the two algorithms are compared. Secondly, the parameters such as population number and crossover mode of p GA are optimized, initial values are set to improve the convergence speed of the algorithm, and the reconstructed image is processed to improve the quality of the image. Finally, experiments are conducted to verify the stability of the p GA under certain noise conditions. In the experiment, different numbers and shapes of targets are placed in the sensor and an EIT system based on 34980A data acquisition system is used for data collection. The image reconstructed by p GA shows that, the position and shape of the targets can accurately correspond to the real object.