Variable neighborhood search for the cost constrained minimum label spanning tree and label constrained minimum spanning tree problems

Authors:
Zahra Naji-Azimi;Majid Salari;Bruce Golden;S. Raghavan;Paolo Toth
Affiliations:
DEIS, University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy;DEIS, University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy;Robert H. Smith School of Business, University of Maryland, College Park, MD 20742-1815, USA;Robert H. Smith School of Business, University of Maryland, College Park, MD 20742-1815, USA;DEIS, University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy
Venue:
Computers and Operations Research
Year:
2010

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Abstract

Given an undirected graph whose edges are labeled or colored, edge weights indicating the cost of an edge, and a positive budget B, the goal of the cost constrained minimum label spanning tree (CCMLST) problem is to find a spanning tree that uses the minimum number of labels while ensuring its cost does not exceed B. The label constrained minimum spanning tree (LCMST) problem is closely related to the CCMLST problem. Here, we are given a threshold K on the number of labels. The goal is to find a minimum weight spanning tree that uses at most K distinct labels. Both of these problems are motivated from the design of telecommunication networks and are known to be NP-complete [15]. In this paper, we present a variable neighborhood search (VNS) algorithm for the CCMLST problem. The VNS algorithm uses neighborhoods defined on the labels. We also adapt the VNS algorithm to the LCMST problem. We then test the VNS algorithm on existing data sets as well as a large-scale dataset based on TSPLIB [12] instances ranging in size from 500 to 1000 nodes. For the LCMST problem, we compare the VNS procedure to a genetic algorithm (GA) and two local search procedures suggested in [15]. For the CCMLST problem, the procedures suggested in [15] can be applied by means of a binary search procedure. Consequently, we compared our VNS algorithm to the GA and two local search procedures suggested in [15]. The overall results demonstrate that the proposed VNS algorithm is of high quality and computes solutions rapidly. On our test datasets, it obtains the optimal solution in all instances for which the optimal solution is known. Further, it significantly outperforms the GA and two local search procedures described in [15].