Transitions in geometric minimum spanning trees
Discrete & Computational Geometry - Special issue on ACM symposium on computational geometry, North Conway
Many birds with one stone: multi-objective approximation algorithms
STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
Advances in linear and integer programming
Advances in linear and integer programming
A network-flow technique for finding low-weight bounded-degree spanning trees
Journal of Algorithms
A weighted coding in a genetic algorithm for the degree-constrained minimum spanning tree problem
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Variable neighborhood search for the degree-constrained minimum spanning tree problem
Discrete Applied Mathematics - Special issue: Third ALIO-EURO meeting on applied combinatorial optimization
Low-Degree Spanning Trees of Small Weight
SIAM Journal on Computing
Comparison of Algorithms for the Degree Constrained Minimum Spanning Tree
Journal of Heuristics
Euclidean bounded-degree spanning tree ratios
Proceedings of the nineteenth annual symposium on Computational geometry
A new evolutionary approach to the degree-constrained minimumspanning tree problem
IEEE Transactions on Evolutionary Computation
Edge sets: an effective evolutionary coding of spanning trees
IEEE Transactions on Evolutionary Computation
A primal branch-and-cut algorithm for the degree-constrained minimum spanning tree problem
WEA'07 Proceedings of the 6th international conference on Experimental algorithms
Degree constrained minimum spanning tree problem: a learning automata approach
The Journal of Supercomputing
The min-degree constrained minimum spanning tree problem: Formulations and Branch-and-cut algorithm
Discrete Applied Mathematics
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In this paper, a Lagrangian-based heuristic is proposed for the degree constrained minimum spanning tree problem. The heuristic uses Lagrangian relaxation information to guide the construction of feasible solutions to the problem. The scheme operates, within a Lagrangian relaxation framework, with calls to a greedy construction heuristic, followed by a heuristic improvement procedure. A look ahead infeasibility prevention mechanism, introduced into the greedy heuristic, allowed us to solve instances of the problem where some of the vertices are restricted to having degrees 1 or 2. Furthermore, in order to cut down on CPU time, a restricted version of the original problem is formulated and used to generate feasible solutions. Extensive computational experiments were conducted and indicate that the proposed heuristic is competitive with the best heuristics and metaheuristics in the literature.