This paper introduces a novel cooperative coevolution (CC) framework for optimizing large-scale nonseparable problems. The framework, denoted as EACC-G, employs a grouping strategy to decompose a high-dimensional objective vector into smaller subcomponents and an adaptive weighting strategy to enhance coadaptation among these subcomponents. The subcomponents are optimized using a self-adaptive differential evolution (SaNSDE) algorithm, which incorporates neighborhood search to improve performance. The effectiveness of the proposed framework is demonstrated through theoretical analysis and extensive computational studies on benchmark functions with up to 1000 dimensions. The results show that EACC-G outperforms existing CC algorithms and conventional evolutionary algorithms in solving high-dimensional optimization problems, particularly for nonseparable functions. The framework's ability to capture variable interdependencies and its scalability to high dimensions make it a promising approach for large-scale evolutionary optimization.This paper introduces a novel cooperative coevolution (CC) framework for optimizing large-scale nonseparable problems. The framework, denoted as EACC-G, employs a grouping strategy to decompose a high-dimensional objective vector into smaller subcomponents and an adaptive weighting strategy to enhance coadaptation among these subcomponents. The subcomponents are optimized using a self-adaptive differential evolution (SaNSDE) algorithm, which incorporates neighborhood search to improve performance. The effectiveness of the proposed framework is demonstrated through theoretical analysis and extensive computational studies on benchmark functions with up to 1000 dimensions. The results show that EACC-G outperforms existing CC algorithms and conventional evolutionary algorithms in solving high-dimensional optimization problems, particularly for nonseparable functions. The framework's ability to capture variable interdependencies and its scalability to high dimensions make it a promising approach for large-scale evolutionary optimization.