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

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

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.

Idioma originalInglés
Título de la publicación alojadaProceedings - 2014 IEEE 26th International Conference on Tools with Artificial Intelligence, ICTAI 2014
EditorialIEEE Computer Society
Páginas24-30
Número de páginas7
ISBN (versión digital)9781479965724
DOI
EstadoPublicada - 12 dic 2014
Evento26th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2014 - Limassol, Chipre
Duración: 10 nov 201412 nov 2014

Serie de la publicación

NombreProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
Volumen2014-December
ISSN (versión impresa)1082-3409

Conferencia

Conferencia26th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2014
País/TerritorioChipre
CiudadLimassol
Período10/11/1412/11/14

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