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In this paper, we present a memetic algorithm with novel local optimizer hybridization strategy for constrained optimization. The developed MA consists of multiple cycles. In each cycle, an estimation of distribution algorithm (EDA) with an adaptive univariate probability model is applied to search for promising search regions. A classical local optimizer, called DONLP2, is applied to improve the best solution found by the EDA to a high quality solution. New cycles are employed when the computational budget has not been reached. The new cycles are expected to learn from the search history to make the further search efficient and to enable escape from local optima. The developed algorithm is experimentally compared with ε-DE, which was the winner of the 2006 IEEE Congress on Evolutionary Computation (CEC'06) competition on constrained optimization. The results favour our algorithm against the best-known algorithm in terms of the number of fitness evaluations used to reach the global optimum.