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An Approach for the Local Exploration of Discrete Many Objective Optimization Problems

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Book cover Evolutionary Multi-Criterion Optimization (EMO 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10173))

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Abstract

Multi-objective optimization problems with more than three objectives, which are also termed as many objective optimization problems, play an important role in the decision making process. For such problems, it is computationally expensive or even intractable to approximate the entire set of optimal solutions. An alternative is to compute a subset of optimal solutions based on the preferences of the decision maker. Commonly, interactive methods from the literature consider the user preferences at every iteration by means of weight vectors or reference points. Besides the fact that mathematical programming techniques only produce one solution at each iteration, they generally require first or second derivative information, that limits its applicability to certain problems. The approach proposed in this paper allows to steer the search into any direction in the objective space for optimization problems of discrete nature. This provides a more intuitive way to set the preferences, which represents a useful tool to explore the regions of interest of the decision maker. Numerical results on multi-objective multi-dimensional knapsack problem instances show the interest of the proposed approach.

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Notes

  1. 1.

    http://www.tik.ee.ethz.ch/sop/download/supplementary/testProblemSuite/.

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Authors and Affiliations

  1. Computer Science Department, CINVESTAV-IPN, Av. IPN 2508, Col. San Pedro Zacatenco, 07360, Mexico City, Mexico

    Oliver Cuate & Oliver Schütze

  2. Univ. Lille, CNRS, Centrale Lille, UMR 9189 – CRIStAL, 59000, Lille, France

    Bilel Derbel, Arnaud Liefooghe & El-Ghazali Talbi

  3. Inria Lille – Nord Europe, 59650, Villeneuve d’Ascq, France

    Bilel Derbel, Arnaud Liefooghe & El-Ghazali Talbi

Authors
  1. Oliver Cuate
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  2. Bilel Derbel
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  3. Arnaud Liefooghe
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  4. El-Ghazali Talbi
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  5. Oliver Schütze
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Corresponding author

Correspondence to Oliver Cuate .

Editor information

Editors and Affiliations

  1. University of Münster, Münster, Germany

    Heike Trautmann

  2. TU Dortmund University, Dortmund, Germany

    Günter Rudolph

  3. University of Wuppertal, Wuppertal, Germany

    Kathrin Klamroth

  4. CINVESTAV-IPN, Mexico City, Mexico

    Oliver Schütze

  5. Clemson University, Clemson, South Carolina, USA

    Margaret Wiecek

  6. University of Surrey, Guildford, United Kingdom

    Yaochu Jin

  7. University of Münster, Münster, Germany

    Christian Grimme

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Cuate, O., Derbel, B., Liefooghe, A., Talbi, EG., Schütze, O. (2017). An Approach for the Local Exploration of Discrete Many Objective Optimization Problems. In: Trautmann, H., et al. Evolutionary Multi-Criterion Optimization. EMO 2017. Lecture Notes in Computer Science(), vol 10173. Springer, Cham. https://doi.org/10.1007/978-3-319-54157-0_10

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  • DOI: https://doi.org/10.1007/978-3-319-54157-0_10

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