Authors: Consoli, Sergio
Moreno Pérez, José Andrés
Mladenović, Nenad 
Title: Comparison of metaheuristics for the k-labeled spanning forest problem
Journal: International Transactions in Operational Research
Volume: 24
Issue: 3
First page: 559
Last page: 582
Issue Date: 1-May-2017
Rank: M21
ISSN: 0969-6016
DOI: 10.1111/itor.12217
In this paper, we study the k-labeled spanning forest (kLSF) problem in which an undirected graph whose edges are labeled and an integer-positive value k are given; the aim is to find a spanning forest of the input graph with the minimum number of connected components and the upper bound (Formula presented.) on the number of labels. The problem is related to the minimum labeling spanning tree problem and has several applications in the real world. In this paper, we compare several metaheuristics to solve this NP-hard problem. In particular, the proposed intelligent variable neighborhood search (VNS) shows excellent performance, obtaining high-quality solutions in short computational running time. This approach integrates VNS with other complementary approaches from machine learning, statistics, and experimental algorithmics, in order to produce high-quality performance and completely automate the resulting optimization strategy.
Keywords: combinatorial optimization | graphs and networks | intelligent optimization | k-labeled spanning forest | minimum labeling spanning trees | variable neighborhood search
Publisher: Wiley

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