This paper presents an improved genetic local algorithm by incorporating the simulated-annealing technique into the perturbation process of the genetic local search algorithm and proposes an improved-genetic-local-search-algorithm-based inverse algorithm for two-dimensional defect reconstruction from the magnetic-flux-leakage signals. In the algorithm, a radial-basis-function neural network is utilized as a forward model, and the improved genetic local search algorithm is used to solve the optimization problem in the inverse problem. Experiments are presented to compare the proposed inverse algorithm with both the canonical-genetic-algorithm-based inverse algorithm and the genetic-local-search-algorithm-based inverse algorithm. The results demonstrate that the proposed inverse algorithm is more accurate and robust to the noise.
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