Effects of Unbounded External Archive on the Performance of Constrained Multiobjective Evolutionary Algorithms

External archives have been demonstrated to be an effective technique for evolutionary multiobjective optimization (EMO) algorithms. Conducting subset selection from external archives offers advantages in finding solution sets that outperform the final population. While previous research has examined the impact of external archives on unconstrained multiobjective optimization problems, their effectiveness on constrained multi-objective optimization problems (CMOPs) remains unexplored. This paper investigates the effects of unbounded external archives (UEAs) and three different subset selection methods on the performance of eight constrained EMO algorithms through computational experiments on artificial and real-world CMOPs. Our experimental results demonstrate that UEAs are effective for constrained EMO algorithms in finding solution sets with improved convergence and diversity in comparison with the final population of each algorithm. It is also demonstrated that the effectiveness of UEAs depends on the constraint handling techniques (CHTs) in EMO algorithms and the choice of subset selection method.