  Electrical Impedance Tomography (EIT) is a noninvasive imaging technique that
does not use ionizing radiation, with application both in environmental
sciences and in health. Image reconstruction is performed by solving an inverse
problem and ill-posed. Evolutionary Computation and Swarm Intelligence have
become a source of methods for solving inverse problems. Fish School Search
(FSS) is a promising search and optimization method, based on the dynamics of
schools of fish. In this article the authors present a method for
reconstruction of EIT images based on FSS and Non-Blind Search (NBS). The
method was evaluated using numerical phantoms consisting of electrical
conductivity images with subjects in the center, between the center and the
edge and on the edge of a circular section, with meshes of 415 finite elements.
The authors performed 20 simulations for each configuration. Results showed
that both FSS and FSS-NBS were able to converge faster than genetic algorithms.
