Node Selection Heuristics Using the Upper Bound in Interval Branch and Bound

We present in this article a new strategy for selecting the current node in an interval Branch and Bound algorithm for constrained global optimization. The standard best-first strategy selects the node with the lowest lower bound of the objective estimate. We propose in this article 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 operators leads to a good performance of the criterion. These new strategies obtain better experimental results than classical best-first search on difficult instances.