Effectiveness and efficiency of non-dominated sorting for evolutionary multi- and many-objective optimization

Abstract

Since non-dominated sorting was first adopted in NSGA in 1995, most evolutionary algorithms have employed non-dominated sorting as one of the major criteria in their environmental selection for solving multi- and many-objective optimization problems. In this paper, we focus on analyzing the effectiveness and efficiency of non-dominated sorting in multi- and many-objective evolutionary algorithms. The effectiveness of non-dominated sorting is verified by considering two popular evolutionary algorithms, NSGA-II and KnEA, which were designed for solving multi- and many-objective optimization problems, respectively. The efficiency of non-dominated sorting is evaluated by comparing several state-of-the-art non-dominated sorting algorithms for multi- and many-objective optimization problems. These results provide important insights to adopt non-dominated sorting in developing novel multi- and many-objective evolutionary algorithms.