A new strategy to detect variable interactions in large scale global optimization

Dynamic Heterogeneous Multi-Population Cultural Algorithm (D-HMP-CA) is a novel optimization algorithm which presents an effective as well as efficient performance to solve large scale global optimization problems. It incorporates dynamic decomposition techniques in order to divide problem dimensions among its local CAs. The variable interactions is not considered in the incorporated dynamic decomposition techniques. In this article, a new strategy is incorporated to detect the variable interactions to improve the process of dimension decomposition. This strategy is integrated into bottom-up dynamic decomposition technique and the integration is called supervised bottom-up approach. The proposed approach is evaluated over the large scale global optimization problems. The evaluation results reveal that the proposed approach outperforms the classical bottom-up technique in solving separable and single-group non-separable optimization functions, while the classical bottom-up approach offers a better performance for multi-group non-separable functions. However, the proposed supervised bottom-up approach presents a more efficient performance compared to the classical bottom-up method which shows that the variable interaction detection strategy does not impose extra computational costs.