Node selection strategies in interval Branch and Bound algorithms

Bertrand Neveu (), Gilles Trombettoni () and Ignacio Araya ()
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Bertrand Neveu: Imagine LIGM Université Paris–Est
Gilles Trombettoni: University of Montpellier
Ignacio Araya: Pontificia Universidad Católica de Valparaíso

Journal of Global Optimization, 2016, vol. 64, issue 2, No 7, 289-304

Abstract: Abstract We present in this article new strategies for selecting nodes in interval Branch and Bound algorithms for constrained global optimization. For a minimization problem the standard best-first strategy selects a node with the smallest lower bound of the objective function 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 node selection rule based on this 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.

Keywords: Intervals; Global optimization; Node selection; Branch and Bound (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10898-015-0375-3

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