Large-scale global optimization (LSGO) is known as one of the most challenging problem for evolutionary algorithms (EA). In this study, we have proposed a novel method of grouping variables for the cooperative coevolution (CC) framework (random adaptive grouping (RAG))). We have implemented the proposed approach in a new evolutionary algorithm (DECC-RAG), which uses the Self-adaptive Differential Evolution (DE) with Neighborhood Search (SaNSDE) as the core search technique. The RAG method is

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... ed on the following idea: after some predefined number of fitness evaluations in cooperative coevolution, a half of subcomponents with the worst fitness values randomly mixes indices of variables, and the corresponding evolutionary algorithms reset adaptation of parameters. We have evaluated the performance of the DECC-RAG algorithm with the large-scale global optimization (LSGO) benchmark problems proposed within the IEEE CEC 2010. The results of numerical experiments are presented and discussed. The results have shown that the proposed algorithm outperforms some popular LSGO approaches.