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International Conference on Computational Science and Its Applications

ICCSA 2020: Computational Science and Its Applications – ICCSA 2020 pp 312–321Cite as

A Biased Random-Key Genetic Algorithm for Bandwidth Reduction

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12249))

Abstract

The bandwidth minimization problem is a well-known \(\mathcal {NP}\)-hard problem. This paper describes our experience in implementing a biased random-key genetic algorithm for the bandwidth reduction problem. Specifically, this paper compares the results of the new algorithm with the results yielded by four approaches. The results obtained on a set of standard benchmark matrices taken from the SuiteSparse sparse matrix collection indicated that the novel approach did not compare favorably with the state-of-the-art metaheuristic algorithm for bandwidth reduction. The former seems to be faster than the latter. On the other hand, the design of heuristics for bandwidth reduction is a very consolidated research area. Thus, a paradigm shift seems necessary to design a heuristic with better results than the state-of-the-art meta-heuristic algorithm at shorter execution times.

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Notes

  1. 1.

    https://www.mathworks.com/help/matlab/ref/symrcm.html?requestedDomain= www.mathworks.com, https://octave.sourceforge.io/octave/function/symrcm.html.

  2. 2.

    http://www.boost.org/doc/libs/1_38_0/libs/graph/doc/cuthill_mckee_ordering.html.

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Author information

Authors and Affiliations

  1. Centro Federal de Educação Tecnológica Celso Suckow da Fonseca, Rio de Janeiro, RJ, Brazil

    P. H. G. Silva, D. N. Brandão & I. S. Morais

  2. Universidade Federal de Lavras, Lavras, MG, Brazil

    S. L. Gonzaga de Oliveira

Authors
  1. P. H. G. Silva
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  2. D. N. Brandão
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  3. I. S. Morais
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  4. S. L. Gonzaga de Oliveira
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Corresponding author

Correspondence to I. S. Morais .

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Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Chair- Center of ICT/ICE, Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Clayton School of Information Technology, Monash University, Clayton, VIC, Australia

    David Taniar

  7. Department of Information Science, Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

  11. Department of Neurology, University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

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Silva, P.H.G., Brandão, D.N., Morais, I.S., de Oliveira, S.L.G. (2020). A Biased Random-Key Genetic Algorithm for Bandwidth Reduction. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12249. Springer, Cham. https://doi.org/10.1007/978-3-030-58799-4_23

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  • DOI: https://doi.org/10.1007/978-3-030-58799-4_23

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  • Online ISBN: 978-3-030-58799-4

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