An Effective Genetic Algorithm for Solving the Clustered Shortest-Path Tree Problem

• Published in: IEEE access 2021, Vol.9, p.15570-15591
• Main Authors: Cosma, Ovidiu, Pop, Petrica C., Zelina, Ioana
• Format: Article
• Language: English
• Subjects: Benchmark testing; Benchmarks; clustered shortest-path tree problem; Clustering algorithms; Combinatorial analysis; Evolutionary computation; Genetic algorithms; Graph theory; Nodes; Optimization; Polynomials; Shortest-path problems; Single-source shortest-path problem; Sociology; Statistics
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3053295

The clustered shortest-path tree problem (CluSPTP) is an extension of the classical single-source shortest-path problem, in which, given a graph with the set of nodes partitioned into a predefined, mutually exclusive and exhaustive set of clusters, we are looking for a shortest-path spanning tree from a given source to all the other nodes of the graph, with the property that each cluster should induce a connected subtree. CluSPTP belongs to the class of generalized combinatorial optimization problems, and, in general, is proved to be a non-deterministic polynomial time hard (NP-hard) problem. In this paper, we propose a novel genetic algorithm (GA), which is designed to fit the challenges of the investigated problem. The main features of our GA are: the use of an innovative representation scheme that allows us to define meaningful genetic operators and the use of a hybrid initial population. Extensive computational results are reported and discussed for two sets of instances: euclidean and non-euclidean. The performance of the proposed algorithm was evaluated on six types of benchmark euclidean instances available in the literature and on six types of non-euclidean instances obtained from the corresponding euclidean ones. The obtained results show an improvement with respect to existing methods from the literature, both in terms of the quality of the achieved solutions and the computation times necessary to obtain them. They demonstrate that our genetic algorithm outperforms all the existing methods from the literature, providing for all the existing benchmark instances the optimal solutions in all 30 independent trials.