"Multi-objective optimization problems (MOPs) widely exist in scientific research and engineering designs. Evolutionary algorithms (EAs) have shown promising potential in solving various MOPs. However, their performance may deteriorate drastically when tackling problems involving a large number of decision variables, i.e., the large-scale multi-objective optimization problems (LSMOPs). In recent years, increasing efforts have been devoted to addressing the challenges brought by such LSMOPs."--
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"Multi-objective optimization problems (MOPs) widely exist in scientific research and engineering designs. Evolutionary algorithms (EAs) have shown promising potential in solving various MOPs. However, their performance may deteriorate drastically when tackling problems involving a large number of decision variables, i.e., the large-scale multi-objective optimization problems (LSMOPs). In recent years, increasing efforts have been devoted to addressing the challenges brought by such LSMOPs."--
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