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AbstractLarge-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 deteriorationdoi:10.1007/s11633-020-1253-0 fatcat:q636cuvarbco7kmnvthnfysa54