Almost all of the multi-objective optimization evolutionary algorithms(MOEAs) are designed for the Pareto optimal front which is distributed uniformly.But in real world optimizations

the Pareto optimal front usually has a non-uniform distribution.A similar solution set distribution with Pareto optimal front is expected to obtain by decision makers.However

the existing algorithms cannot solve such problems effectively.In this paper

a diversity maintenance method for non-uniformly distributed multi-objective optimization problem(NUDMM) is proposed.In the algorithm

an indicator reflecting 'regular’ degree of distribution-Messy is defined.And a method to decrease Messy of population is designed

which eliminates disordered individual on the condition that the distribution of the Pareto optimal front is unknown.From an extensive comparative study with NSGA-II and SPEA2 on eight non-uniform distribution test problems

it is observed that the proposed method has a good performance in maintaining the real distribution and convergence.