Memory enhanced estimation of distribution algorithm in dynamic environments

A memory enhanced estimation of distribution algorithm (M-EDA) is proposed to solve binary-coded dynamic optimization problems (DOPs), in which a probability model is treated as the basic memory element and reused in new environments. A memory management scheme based on environment identification method is designed and the population diversity is compensated dynamically. The experiment results show the universal property of the M-EDA, and verify the ability of the diversity compensation methods to maintain the diversity of the population. In the experiments on five dynamic optimization problems, M-EDA performs significantly better than other two state-of-art dynamic evolutionary algorithms.