Clustering-Based Particle Swarm Optimization for Electrical Impedance Imaging

An attempt has been made in this paper to solve the non-linear and ill-posed Electrical Impedance Tomography (EIT) inverse problem using clustering-based particle swam optimization (PSO). To enhance optimal search capability in such an ultra high dimension space and improve the quality of the reconstructed image, an adaptive PSO algorithm combined with a modified Newton-Raphson algorithm and a conductivity-based clustering algorithm was proposed. The modifications are performed on the reduction of dimension by dividing all mesh into clusters and initializing particles using the result of the modified Newton-Raphson type algorithm. Numerical simulation results indicated that the proposed method has a faster convergence to optimal solution and higher spatial resolution on a reconstructed image.