On the Quality of Large Non-dominated Archives

This study explores the characteristics of the extensive non-dominated archive generated by evolutionary multi-objective optimization (EMO) algorithms. Our findings reveal that such archives typically contain not only high-quality solutions positioned near the Pareto front, but also less desirable ones that deviate significantly from it. We further evaluate the effectiveness of various subset selection strategies in isolating the high-quality solutions within the archive. The insights gained from this work aim to assist EMO practitioners in gaining a deeper understanding of archive composition and in selecting appropriate subset selection methods to support decision-making.