A fast search algorithm for the quorumcast routing problem
Information Processing Letters
An efficient QoS routing algorithm for quorumcast communication
Computer Networks: The International Journal of Computer and Telecommunications Networking
Handbook of Constraint Programming (Foundations of Artificial Intelligence)
Handbook of Constraint Programming (Foundations of Artificial Intelligence)
The "Not-Too-Heavy Spanning Tree" Constraint
CPAIOR '07 Proceedings of the 4th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Obtaining optimal k-cardinality trees fast
Journal of Experimental Algorithmics (JEA)
An efficient algorithm for constructing delay bounded minimum cost multicast trees
Journal of Parallel and Distributed Computing
Improving linear programming approaches for the steiner tree problem
WEA'03 Proceedings of the 2nd international conference on Experimental and efficient algorithms
CPAIOR'08 Proceedings of the 5th international conference on Integration of AI and OR techniques in constraint programming for combinatorial optimization problems
The minimum spanning tree constraint
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
Solving connected subgraph problems in wildlife conservation
CPAIOR'10 Proceedings of the 7th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
On finding minimum cost tree for multi-resource manycast in mesh networks
Optical Switching and Networking
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The quorumcast routing problem is a generalization of multicasting which arises in many distributed applications. It consists of finding a minimum cost tree that spans the source node r and at least q out of m specified nodes on a given undirected weighted graph. This paper proposes a complete and an incomplete approach, both based on the same Constraint Programming (CP) model, but with two different specific search heuristics based on shortest paths. Experimental results show the efficiency of the two proposed approaches. Our complete approach (CP model + complete search) is better than the state of the art complete algorithm and our incomplete approach (CP model + incomplete search) is better than the state of the art incomplete algorithm. Moreover, the proposed complete search is better than the standard First-Fail search in the same CP model.