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Understanding Instance Complexity in the Linear Ordering Problem

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Intelligent Data Engineering and Automated Learning – IDEAL 2013 (IDEAL 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8206))

Abstract

The Linear Ordering Problem is a combinatorial optimization problem which has been frequently addressed in the literature due to its numerous applications in diverse fields. In spite of its popularity, little is known about its complexity. In this paper we analyze the linear ordering problem trying to identify features or characteristics of the instances that can provide useful insights into the difficulty of solving them. Particularly, we introduce two different metrics, insert ratio and ubiquity ratio, that measure the difficulty of solving the LOP with local search type algorithms with the insert neighborhood system. Conducted experiments demonstrate that the proposed metrics clearly correlate with the complexity of solving the LOP with a multistart local search algorithm.

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Author information

Authors and Affiliations

  1. Intelligent Systems Group, Department of Computer Science and Artificial Intelligence, University of the Basque Country UPV/EHU, Donostia, 20018, Gipuzkoa, Spain

    Josu Ceberio, Leticia Hernando, Alexander Mendiburu & Jose A. Lozano

Authors
  1. Josu Ceberio
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  2. Leticia Hernando
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  3. Alexander Mendiburu
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  4. Jose A. Lozano
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

  2. University of Science and Technology of China, Hefei, China

    Ke Tang

  3. Nanjing University, Nanjing, China

    Yang Gao

  4. Ostfalia University of Applied Sciences, 38302, Wolfenbüttel, Germany

    Frank Klawonn

  5. Kyungpook National University, 702-701, Buk-Gu, Daegu, Korea

    Minho Lee

  6. Nature Inspired Computational and Applications Laboratory, School of Computer Science and Technology,, University of Science and Technology of China, 230027, Hefei, China

    Thomas Weise

  7. University of Science and Technology of China, 230017, Hefei, China

    Bin Li

  8. CERCIA, School of Computer Science, University of Birmingham, B15 2TT, Edgbaston, Birmingham, UK

    Xin Yao

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© 2013 Springer-Verlag Berlin Heidelberg

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Ceberio, J., Hernando, L., Mendiburu, A., Lozano, J.A. (2013). Understanding Instance Complexity in the Linear Ordering Problem. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2013. IDEAL 2013. Lecture Notes in Computer Science, vol 8206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41278-3_58

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  • DOI: https://doi.org/10.1007/978-3-642-41278-3_58

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41277-6

  • Online ISBN: 978-3-642-41278-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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