Relaxed dominance relation based evolutionary algorithms for many-objective optimization (MaOEAs) are widely concerned because they effectively improve the ability to identify solutions

however

the most of these algorithms show poor versatility in solving different objectives of optimization problems. To address this issue

this paper proposes an adaptive dominance criterion based evolutionary algorithm for many-objective optimization (ADCEA). Firstly

the ADC designs an adaptive niche method based on the angle information and the number of objectives between adjacent solutions in the objective space

and combines the convergence indicator information to achieve non-dominated sorting of candidate solutions. Then

in order to further enhance the diversity of the population

the reference vector population technique is introduced in the environment selection. Finally

a reasonable fitness function is constructed

and the non-dominated solution set with better convergence and diversity is selected according to the fitness value. The experimental results demonstrate that the proposed method improves the versatility in solving different objectives of optimization problems

and achieves significant effects in balancing convergence and diversity.