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Large-scale multi-objective optimization problems (MOPs) that involve a large number of decision variables, have emerged from many real-world applications. While evolutionary algorithms (EAs) have been widely acknowledged as a mainstream method for MOPs, most  research progress and  successful applications of EAs have been  restricted  to MOPs with  small-scale decision variables. More recently,  it has been reported that traditional multi-objective EAs (MOEAs) suffer severe deterioration with the  increase of decision variables. As a result, and motivated by the emergence of real-world large-scale MOPs, investigation of MOEAs in this aspect has attracted much more attention in the past decade. This paper reviews the progress of evolutionary computation for large-scale multi-objective optimization from two angles. From the key difficulties of the large-scale MOPs, the scalability analysis is discussed by focusing on the performance of existing MOEAs and the challenges  induced by the  increase of the number of decision variables. From the perspective  of methodology,  the  large-scale MOEAs  are  categorized  into  three  classes  and  introduced  respectively:  divide  and  conquer based, dimensionality reduction based and enhanced search-based approaches. Several future research directions are also discussed.

Evolutionary Computation for Large-scale Multi-objective Optimization: A Decade of Progresses