  While Branch and Bound based algorithms are a standard approach to solve
single-objective (mixed-)integer optimization problems, multi-objective Branch
and Bound methods are only rarely applied compared to the predominant objective
space methods. In this paper we propose modifications to increase the
performance of multi-objective Branch and Bound algorithms by utilizing
scalarization-based information. We use the hypervolume indicator as a measure
for the gap between lower and upper bound set to implement a multi-objective
best-first strategy. By adaptively solving scalarizations in the root node to
integer optimality we improve both, upper and lower bound set. The obtained
lower bound can then be integrated into the lower bounds of all active nodes,
while the determined solution is added to the upper bound set. Numerical
experiments show that the number of investigated nodes can be significantly
reduced by up to 83% and the total computation time can be reduced by up to
80%.
