A Divisive Multi-level Differential Evolution

It is generally accepted that the clustering-based differential evolution (CDE) algorithm exhibits better performance in comparison with the standard differential evolution. However, such clustering method mechanism that is only based on input data may lead to some limitations such as premature convergence. In this study, we propose a divisive multi-level differential evolution algorithm (DMDE) to alleviate this drawback. The proposed divisive method is based not only input data but also the output fitness. In particular, DMDE becomes the conventional CDE when the output fitness in not considered in the process of clustering. Several benchmark functions are included to evaluate the performance of the proposed DMDE. Experimental results show that the proposed DMDE exhibits a promising performance when compared with CDE, especially in case of high-dimensional continuous optimization problems.

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