Defect reconstruction from MFL signals using an improved genetic local search algorithm

This paper presents an improved GLSA (IGLSA) by incorporating the simulated annealing technique into the perturbation process of the genetic local search (GLSA), and proposes an IGLSA-based inverse algorithm for 2-D defect reconstruction from the magnetic flux leakage (MFL) signals. In the algorithm, radial-basis function neural network (RBFNN) is utilized as forward model, and the IGLSA is used to solve the optimization problem in the inverse problem. Experiments are presented to show the performance of the IGLSA-based inverse algorithm and to compare it with the canonical-genetic-algorithm based (CGA-based) inverse algorithm and the GLSA-based inverse algorithm, respectively. The results demonstrate that IGLSA-based inverse algorithm is more accurate and is robust to the noise.