Previous methods are difficult to tackle a many-objective optimization problem since it contains many objectives.A set-based evolutionary algorithm was proposed to effectively solve the above problem in this study.In the proposed method

the original optimization problem was first transformed into a tri-objective one by taking such indicators as hyper-volume

distribution and spread as three new objectives;thereafter

a set-based Pareto dominance relation was defined

and a fitness function reflecting a user's preference was designed;additionally

set-based evolutionary strategies were suggested.The proposed method was applied to four benchmark many-objective optimization problems and compared with the other two methods.The experimental results show its advantages.