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The performance of cooperative co-evolutionary (CC) algorithms for large-scale continuous optimization is significantly affected by the adopted decomposition of the search space. According to the literature, a typical decomposition in case of separable problems consists of adopting equally sized subcomponents for the whole optimization process (i.e. static decomposition). Such an approach is also often used for non-separable problems, together with a random-grouping strategy. More advanced methods try to determine the optimal size of subcomponents during the optimization process using reinforcement-learning techniques. However, the latter approaches are not always suitable in this case because of the non-stationary and history-dependent nature of the learning environment. This paper investigates a new CC algorithm, based on Differential Evolution, in which several decompositions are applied in parallel during short learning phases. The experimental results on a set of large-scale optimization problems show that the proposed method can lead to a reliable estimate of the suitability of each subcomponent size. Moreover, in some cases it outperforms the best static decomposition

A Cooperative Coevolutionary Differential Evolution Algorithm with Adaptive Subcomponents