Comparative Study of Heuristic Algorithms for Electrical Impedance Tomography
Based on electrical measurements from electrodes placed around the boundary of a body, electrical impedance tomography (EIT) is an imaging procedure that recovers the spatial distribution of the conductivities in the interior of a body. Recent studies have shown promising results in reconstructing EIT images using heuristic algorithms. This work presents a study of the applicability of six heuristic algorithms firefly algorithm (FA) novel bat algorithm (NBA) genetic algorithm with new multi parent crossover (GA MPC) success history based adaptive differential evolution with linear population size reduction with semiparameter adaptation hybrid with covariance matrix adaptation evolutionary strategy (LSHADE SPACMA) ensemble sinusoidal differential covariance matrix adaptation (LSHADE cnEpSin) and effective butterfly optimizer with covariance matrix adapted retreat phase (EBOwithCMAR) for the EIT image reconstruction problem. These algorithms have never been employed to solve the EIT inverse problem. Series of numerical tests were carried out to compare the performance of the selected algorithms.