A hybrid ensemble approach for the Steiner tree problem in large graphs: A geographical application

Authors:
Abdelhamid Bouchachia;Markus Prossegger
Affiliations:
University of Klagenfurt, Department of Informatics, Group of Software Engineering and Soft Computing, Klagenfurt 9020, Austria;Carinthia University of Applied Sciences, School of Network Engineering and Communication, Klagenfurt 9020, Austria
Venue:
Applied Soft Computing
Year:
2011

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Abstract

Abstract: Hybrid approaches are often recommended for dealing in an efficient manner with complex problems that require considerable computational time. In this study, we follow a similar approach consisting of combining spectral clustering and ant colony optimization in a two-stage algorithm for the purpose of efficiently solving the Steiner tree problem in large graphs. The idea of the two-stage approach, called ESC-IAC, is to apply a divide-and-conquer strategy which consists of breaking down the problem into sub-problems to find local solutions before combining them. In the first stage, graph segments (clusters) are generated using an ensemble spectral clustering method for enhancing the quality; whereas in the second step, parallel independent ant colonies are implemented to find local and global minima of the Steiner tree. To illustrate the efficiency and accuracy, ESC-IAC is applied in the context of a geographical application relying on real-world as well as artificial benchmarks.