Node selection in interval Branch and Bound algorithms

We present in this article new strategies for selecting nodes in interval Branch and Bound algorithms for constrained global optimization. The standard best-first strategy selects a node with the lowest lower bound of the objective estimate. We first propose new node selection policies where an upper bound of each node/box is also taken into account. The good accuracy of this upper bound achieved by several contracting operators leads to a good performance of the criterion. We propose another strategy that also makes a tradeoff between diversification and intensification by greedily diving into potential feasible regions at each node of the best-first search. These new strategies obtain better experimental results than classical best-first search on difficult constrained global optimization instances.