A hierarchical clustering-based heuristic for the Euclidean BDMST problem is given by Gruber and Raidi , which obtains low cost BDSTs when the diameter constraint is very small.
This paper presents some fast heuristics for the Euclidean BDMST problem.
This section presents several well known heuristics for the BDMST Problem and summarizes their key characteristics.
The Euclidean Steiner Problem data sets given in Beasley's OR-Library (1) have been used extensively in the literature for benchmarking heuristics and algorithms for the BDMST Problem.
Cloud State University, Minnesota, USA for providing the extended suite of problem instances for the BDMST problem.