NSGA-II is, by far, the most popular metaheuristic that has been adopted for solving
multi-objective optimization problems. However, its most common usage, particularly when dealing
with continuous problems, is circumscribed to a standard algorithmic configuration similar to the
one described in its seminal paper. In this work, our aim is to show that the performance of NSGA-II,
when properly configured, can be significantly improved in the context of large-scale optimization.
It leverages a combination of tools for automated algorithmic tuning called irace, and a highly
configurable version of NSGA-II available in the jMetal framework. Two scenarios are devised: first,
by solving the Zitzler–Deb–Thiele (ZDT) test problems, and second, when dealing with a binary realworld
problem of the telecommunications domain. Our experiments reveal that an auto-configured
version of NSGA-II can properly address test problems ZDT1 and ZDT2 with up to 217 = 131, 072
decision variables. The same methodology, when applied to the telecommunications problem, shows
that significant improvements can be obtained with respect to the original NSGA-II algorithm when
solving problems with thousands of bits.
