Estimating Upper Bounds for Improving the Filtering in Interval Branch and Bound Optimizers

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Abstract

When interval branch and bound solvers are used for solving constrained global optimization, upper bounding the objective function is an important mechanism which helps to reduce globally the search space. Each time a new upper bound UB is found during the search, a constraint related to the objective function fobj (x). & UB is added in order to prune non-optimal regions. We quantified experimentally that if we knew a close-to-optimal value in advance (without necessarily knowing the corresponding solution), then the performance of the solver could be significantly improved. Thus, in this work we propose a simple mechanism for estimating upper bounds in order to accelerate the convergence of interval branch and bound solvers. The proposal is validated through a series of experiments.

Original languageEnglish
Title of host publicationProceedings - 2014 IEEE 26th International Conference on Tools with Artificial Intelligence, ICTAI 2014
PublisherIEEE Computer Society
Pages24-30
Number of pages7
ISBN (Electronic)9781479965724
DOIs
StatePublished - 12 Dec 2014
Event26th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2014 - Limassol, Cyprus
Duration: 10 Nov 201412 Nov 2014

Publication series

NameProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
Volume2014-December
ISSN (Print)1082-3409

Conference

Conference26th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2014
CountryCyprus
CityLimassol
Period10/11/1412/11/14

Keywords

  • branch & bound
  • global optimization
  • interval-based solvers
  • upper bounding

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