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A wide range of real-world problems are multi-objective optimization problems (MOPs). Multi-objective evolutionary algorithms (MOEAs) have been proposed to solve MOPs, but the search process deteriorates with the increase of MOPs' dimension of decision variables. In order to solve the problem, firstly, the decision variables are divided into different groups by adopting a fast interdependency identification algorithm; secondly, a novel cooperative co-evolutionary algorithm is used to solve MOPs. Experiment results on large-scale problems show that the proposed algorithm is effective.
Li et al. (2018) studied this question.