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.
A Comparison of Heuristic Methods for the Prize-Collecting Steiner Tree Problem and Their Application in Genomics / Akhmedov, Murodzhon; Kwee, Ivo; Montemanni, Roberto. - (2017), pp. 101-108. (Intervento presentato al convegno Operations Research 2015 tenutosi a Wien, Austria nel September 2015) [10.1007/978-3-319-42902-1_14].
A Comparison of Heuristic Methods for the Prize-Collecting Steiner Tree Problem and Their Application in Genomics
Montemanni, Roberto
2017
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.
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