Intelligent optimization algorithm approach to image reconstruction in electrical impedance tomography

In electrical impedance tomography (EIT), various image reconstruction algorithms have been used in order to compute the internal resistivity distribution of the unknown object with its electric potential data at the boundary. Mathematically the EIT image reconstruction algorithm is a nonlinear ill-posed inverse problem. This paper presents two intelligent optimization algorithm techniques such as genetic algorithm (GA) and simulated annealing (SA) for the solution of the static EIT inverse problem. We summarize the simulation results for the modified Newton-Raphson, GA, and SA algorithms.