  The evolutionary diversity optimization aims at finding a diverse set of
solutions which satisfy some constraint on their fitness. In the context of
multi-objective optimization this constraint can require solutions to be
Pareto-optimal. In this paper we study how the GSEMO algorithm with additional
diversity-enhancing heuristic optimizes a diversity of its population on a
bi-objective benchmark problem OneMinMax, for which all solutions are
Pareto-optimal.
  We provide a rigorous runtime analysis of the last step of the optimization,
when the algorithm starts with a population with a second-best diversity, and
prove that it finds a population with optimal diversity in expected time
$O(n^2)$, when the problem size $n$ is odd. For reaching our goal, we analyse
the random walk of the population, which reflects the frequency of changes in
the population and their outcomes.
