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A Comparison of Heuristic Methods for the Prize-Collecting Steiner Tree Problem and Their Application in Genomics

In: Operations Research Proceedings 2015

Author

Listed:
  • Murodzhon Akhmedov

    (Dalle Molle Institute for Artificial Intelligence (IDSIA-USI/SUPSI)
    Institute of Oncology Research (IOR))

  • Ivo Kwee

    (Dalle Molle Institute for Artificial Intelligence (IDSIA-USI/SUPSI)
    Institute of Oncology Research (IOR))

  • Roberto Montemanni

    (Dalle Molle Institute for Artificial Intelligence (IDSIA-USI/SUPSI))

Abstract

The prize-collecting Steiner tree (PCST) problem is a broadly studied problem in combinatorial optimization. It has been used to model several real world problems related to utility networks. More recently, researchers have started using PCSTs to study biological networks. Biological networks are typically very large in size. This can create a considerable challenge for the available PCST solving methods. Taking this fact into account, we have developed methods for the PCST that efficiently scale up to large biological network instances. Namely, we have devised a heuristic method based on the Minimum Spanning Tree and a matheuristic method composed of a heuristic clustering phase and a solution phase. In this work, we provide a performance comparison for these methods by testing them on large gene interaction networks. Experimental results are reported for the methods, including running times and objective values of the solutions.

Suggested Citation

  • Murodzhon Akhmedov & Ivo Kwee & Roberto Montemanni, 2017. "A Comparison of Heuristic Methods for the Prize-Collecting Steiner Tree Problem and Their Application in Genomics," Operations Research Proceedings, in: Karl Franz Dörner & Ivana Ljubic & Georg Pflug & Gernot Tragler (ed.), Operations Research Proceedings 2015, pages 101-108, Springer.
    • Handle: RePEc:spr:oprchp:978-3-319-42902-1_14
    DOI: 10.1007/978-3-319-42902-1_14
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