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While Branch and Bound based algorithms are a standard approach to solvesingle-objective (mixed-)integer optimization problems, multi-objective Branchand Bound methods are only rarely applied compared to the predominant objectivespace methods. In this paper we propose modifications to increase theperformance of multi-objective Branch and Bound algorithms by utilizingscalarization-based information. We use the hypervolume indicator as a measurefor the gap between lower and upper bound set to implement a multi-objectivebest-first strategy. By adaptively solving scalarizations in the root node tointeger optimality we improve both, upper and lower bound set. The obtainedlower bound can then be integrated into the lower bounds of all active nodes,while the determined solution is added to the upper bound set. Numericalexperiments show that the number of investigated nodes can be significantlyreduced by up to 83% and the total computation time can be reduced by up to80%.

Augmenting Bi-objective Branch and Bound by Scalarization-Based Information