Computers and Operations Research
Variable Neighborhood Decomposition Search
Journal of Heuristics
A new hybrid evolutionary algorithm for the huge k-cardinality tree problem
Proceedings of the 8th annual conference on Genetic and evolutionary computation
An annotated bibliography of combinatorial optimization problems with fixed cardinality constraints
Discrete Applied Mathematics - Special issue: 2nd cologne/twente workshop on graphs and combinatorial optimization (CTW 2003)
Local and variable neighborhood search for the k-cardinality subgraph problem
Journal of Heuristics
Obtaining optimal k-cardinality trees fast
Journal of Experimental Algorithmics (JEA)
An annotated bibliography of combinatorial optimization problems with fixed cardinality constraints
Discrete Applied Mathematics - Special issue: 2nd cologne/twente workshop on graphs and combinatorial optimization (CTW 2003)
An approximate solution method based on tabu search for k-minimum spanning tree problems
International Journal of Knowledge Engineering and Soft Data Paradigms
Memetic algorithms for constructing binary covering arrays of strength three
EA'09 Proceedings of the 9th international conference on Artificial evolution
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
A variable neighbourhood search algorithm for job shop scheduling problems
EvoCOP'06 Proceedings of the 6th European conference on Evolutionary Computation in Combinatorial Optimization
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The minimum k-cardinality tree problem on graph G consists in finding a subtree of G with exactly k edges whose sum of weights is minimum. A number of heuristic methods have been developed recently to solve this NP-hard problem. In this paper a decomposition approach is developed and implemented within a successive approximation scheme known as variable neighborhood decomposition search. This approach obtains superior results over existing methods, and furthermore, allows larger problem instances(up to 5000 nodes) to be solved more efficiently.