Exploiting hierarchical clustering for finding bounded diameter minimum spanning trees on euclidean instances

The bounded diameter minimum spanning tree problem is an NP-hard combinatorial optimization problem arising, for example, in network design when quality of service is of concern. There exist various exact and metaheuristic approaches addressing this problem, whereas fast construction heuristics are primarily based on Prim’s minimum spanning tree algorithm and fail to produce reasonable solutions in particular on large Euclidean instances. A method based on hierarchical clustering to guide the construction process of a diameter constrained tree is presented. Solutions obtained are further refined using a greedy randomized adaptive search procedure. Based on the idea of clustering we also designed a new neighborhood search for this problem. Especially on large Euclidean instances with a tight diameter bound the results are excellent. In this case the solution quality can also compete with that of a leading metaheuristic, whereas the computation only needs a fraction of the time. Cate...

• Type: Conference
• Year: 2009
• Where: GECCO
• Authors: Martin Gruber, Günther R. Raidl
• Keywords: Artificial Intelligence, GECCO 2009, Greedy Randomized Adaptive Search Procedure, Large Euclidean Instances, Minimum Spanning Tree