Volume 55, Number 4, July-August 2021
|2247 - 2264
|29 July 2021
Low-cost heuristics for matrix bandwidth reduction combined with a Hill-Climbing strategy
Universidade Federal de Lavras, Lavras, Brazil
* Corresponding author: firstname.lastname@example.org
Accepted: 9 July 2021
This paper studies heuristics for the bandwidth reduction of large-scale matrices in serial computations. Bandwidth optimization is a demanding subject for a large number of scientific and engineering applications. A heuristic for bandwidth reduction labels the rows and columns of a given sparse matrix. The algorithm arranges entries with a nonzero coefficient as close to the main diagonal as possible. This paper modifies an ant colony hyper-heuristic approach to generate expert-level heuristics for bandwidth reduction combined with a Hill-Climbing strategy when applied to matrices arising from specific application areas. Specifically, this paper uses low-cost state-of-the-art heuristics for bandwidth reduction in tandem with a Hill-Climbing procedure. The results yielded on a wide-ranging set of standard benchmark matrices showed that the proposed strategy outperformed low-cost state-of-the-art heuristics for bandwidth reduction when applied to matrices with symmetric sparsity patterns.
Mathematics Subject Classification: 05C78 / 90C27
Key words: Bandwidth reduction / sparse matrix / ant colony optimization / hyper-heuristic / reordering algorithms / renumbering / ordering / graph labeling / graph algorithm / local search procedure / Hill-Climbing procedure
© The authors. Published by EDP Sciences, ROADEF, SMAI 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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