An effective approach for adapting the size of subcomponents in large-scale optimization with cooperative coevolution

The performance of cooperative co-evolutionary algorithms for large-scale global optimization (LSGO) can be significantly affected by the adopted problem decomposition. This study investigates a new adaptive Cooperative Coevolutionary algorithm in which several decompositions are concurrently applied during short learning phases. Moreover, the study includes some experimental results on a set of LSGO problems and a comparison with a recent approach based on reinforcement-learning. According to the numerical results, the proposed adaptive approach can provide a superior search efficiency on several benchmark functions.