  The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is the most
prominent multi-objective evolutionary algorithm for real-world applications.
While it performs evidently well on bi-objective optimization problems,
empirical studies suggest that it is less effective when applied to problems
with more than two objectives. A recent mathematical runtime analysis confirmed
this observation by proving the NGSA-II for an exponential number of iterations
misses a constant factor of the Pareto front of the simple 3-objective
OneMinMax problem.
  In this work, we provide the first mathematical runtime analysis of the
NSGA-III, a refinement of the NSGA-II aimed at better handling more than two
objectives. We prove that the NSGA-III with sufficiently many reference points
-- a small constant factor more than the size of the Pareto front, as suggested
for this algorithm -- computes the complete Pareto front of the 3-objective
OneMinMax benchmark in an expected number of O(n log n) iterations. This result
holds for all population sizes (that are at least the size of the Pareto
front). It shows a drastic advantage of the NSGA-III over the NSGA-II on this
benchmark. The mathematical arguments used here and in previous work on the
NSGA-II suggest that similar findings are likely for other benchmarks with
three or more objectives.
