Computing nonlinear lts estimator based on a random differential evolution strategy

Nonlinear least trimmed squares (NLTS) estimator is a very important kind of nonlinear robust estimator, which is widely used for recovering an ideal high-quality signal from contaminated data. However, the NLTS estimator has not been widely used because it is hard to compute. This paper develops an algorithm to compute the NLTS estimator based on a random differential evolution (DE) strategy. The strategy which uses random DE schemes and control variables improves the DE performance. The simulation results demonstrate that the algorithm gives better performance and is more convenient than existing computing algorithms for the NLTS estimator. The algorithm makes the NLTS estimator easy to apply in practice, even for large data sets, e.g. in a data mining context.