Integer and combinatorial optimization
Integer and combinatorial optimization
Partial constraint satisfaction
Artificial Intelligence - Special volume on constraint-based reasoning
Maintaining reversible DAC for Max-CSP
Artificial Intelligence
A General Scheme for Multiple Lower Bound Computation in Constraint Optimization
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
Directed Arc Consistency Preprocessing
Constraint Processing, Selected Papers
Constraint satisfaction as global optimization
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Enhancements of branch and bound methods for the maximal constraint satisfaction problem
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A new honeybee optimization for constraint reasoning: case of max-CSPs
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
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The inefficiency of the branch and bound method for solving Constraint Optimization Problems is due in most cases to the poor quality of the lower bound used by this method. Many works have been proposed to improve the quality of this bound. In this, paper we investigate a set of lower bounds by considering two criteria: the quality and the computing cost. We study different ways to compute the parameters of the parametric lower bound and we propose heuristics for searching the parameters maximizing the parametric lower bound. Computational experiments performed over randomly generated problems show the advantages of our new branch and bound scheme.