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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 thedoi:10.1109/cec.2010.5585938 dblp:conf/cec/SunG10 fatcat:q6lrks2s7vcl3ov2zubqvvmqwe