  Constrained single-objective problems have been frequently tackled by
evolutionary multi-objective algorithms where the constraint is relaxed into an
additional objective. Recently, it has been shown that Pareto optimization
approaches using bi-objective models can be significantly sped up using sliding
windows (Neumann and Witt, ECAI 2023). In this paper, we extend the sliding
window approach to $3$-objective formulations for tackling chance constrained
problems. On the theoretical side, we show that our new sliding window approach
improves previous runtime bounds obtained in (Neumann and Witt, GECCO 2023)
while maintaining the same approximation guarantees. Our experimental
investigations for the chance constrained dominating set problem show that our
new sliding window approach allows one to solve much larger instances in a much
more efficient way than the 3-objective approach presented in (Neumann and
Witt, GECCO 2023).
