A new algorithm for adapting the configuration of subcomponents in large-scale optimization with cooperative coevolution

The cooperative coevolutionary (CC) approach can be very effective in solving problems of large-scale continuous optimization (LSGO) through their decomposition into lower-dimensional subcomponents. However, it is well known that the CC performance can be significantly influenced by the adopted decomposition. Moreover, since the method may require evolving a number of populations, also the size of the latter can largely affect the optimization process. In this article, focusing on equally sized decompositions, we present the results of an in-depth investigation concerning the effects of both the size of populations and the dimensionality of subcomponents on the performance of a CC optimizer. According to our study, in several cases only a small set of suitable configurations corresponds to a high optimization performance. Furthermore, we propose a new CC algorithm in which part of the available computational budget is spent for adapting both the dimensionality of subcomponents and the number of evolved individuals during the optimization process. Using a rich set of benchmark problems, we show that the proposed approach can outperform a state-of-the art algorithm based on adaptive equally sized decompositions.