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UMDAs for dynamic optimization problems

This paper investigates how the Univariate Marginal Distribution Algorithm (UMDA) behaves in non-stationary environments when engaging in sampling and selection strategies designed to correct diversity loss. Although their performance when solving Dynamic Optimization Problems (DOP) is less studied than population-based Evolutionary Algorithms, UMDA and other Estimation of Distribution Algorithms may follow similar schemes when tracking moving optima: genetic diversity maintenance, memory schemes, niching methods, and even reinicialization of the probability vectors. This study is focused on diversity maintenance schemes. A new update strategy for UMDA's probability model, based on Ant Colony Optimization transition probability equations, is presented and empirically compared with other strategies recently published that aim to correct diversity loss in UMDA. Results demonstrate that loss correction strategies delay or avoid full convergence, thus increasing UMDA's adaptability to changing environments. However, the strategy proposed in this paper achieves a higher performance on the DOP test set when compared with other methods. In addition, the new strategy incorporates two parameters that control the diversity of the probability model.