Skewed VNS enclosing second order algorithm for the degree constrained minimum spanning tree problem

We develop ideas to enhance the performance of the variable neighborhood search (VNS) by guiding the search in the shaking phase, and by employing the Skewed strategy. For this purpose, Second Order algorithms and Skewed functions expressing structural differences are embedded in an efficient VNS pr...

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Vydáno v:European journal of operational research Ročník 191; číslo 3; s. 677 - 690
Hlavní autoři: de Souza, Mauricio C., Martins, Pedro
Médium: Journal Article Konferenční příspěvek
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 16.12.2008
Elsevier
Elsevier Sequoia S.A
Edice:European Journal of Operational Research
Témata:
Applied sciences
Degree constrained minimum spanning tree
Exact sciences and technology
Flows in networks. Combinatorial problems
Graph theory
Operational research and scientific management
Operational research. Management science
Optimization algorithms
Second order algorithm
Skewed VNS
Studies
Variable neighborhood search
VNS shaking phase
Second order algorithm
VNS shaking phase
Skewed VNS
Variable neighborhood search
Degree constrained minimum spanning tree
Lower bound
Tree(graph)
Graph theory
Weighted graph
Constrained optimization
Neighborhood search
Spanning tree
Non directed graph
ISSN:0377-2217, 1872-6860
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Shrnutí:We develop ideas to enhance the performance of the variable neighborhood search (VNS) by guiding the search in the shaking phase, and by employing the Skewed strategy. For this purpose, Second Order algorithms and Skewed functions expressing structural differences are embedded in an efficient VNS proposed in the literature for the degree constrained minimum spanning tree problem. Given an undirected graph with weights associated with its edges, the degree constrained minimum spanning tree problem consists in finding a minimum spanning tree of the given graph, subject to constraints on node degrees. Computational experiments are conducted on the largest and hardest benchmark instances found in the literature to date. We report results showing that the VNS with the proposed strategies improved the best known solutions for all the hardest benchmark instances. Moreover, these new best solutions significantly reduced the gaps with respect to tight lower bounds reported in the literature.
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ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2006.12.061