关键词: 松弛支配, 高维多目标进化算法, 普适性, 自适应支配准则

Abstract: 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.

Key words: relaxed dominance relations, many-objective evolutionary algorithms, versatility, adaptive dominance criterion