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Analysis of Bayesian Network Learning Techniques for a Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm: a case study on MNK Landscape

Author

Listed:
  • Marcella S. R. Martins

    (Federal University of Technology - Paraná (UTFPR))

  • Mohamed El Yafrani

    (Aalborg University (AAU))

  • Myriam Delgado

    (Federal University of Technology - Paraná (UTFPR))

  • Ricardo Lüders

    (Federal University of Technology - Paraná (UTFPR))

  • Roberto Santana

    (University of the Basque Country (UPV/EHU))

  • Hugo V. Siqueira

    (Federal University of Technology - Paraná (UTFPR))

  • Huseyin G. Akcay

    (Akdeniz University (AKU))

  • Belaïd Ahiod

    (Mohammed V University in Rabat)

Abstract

This work investigates different Bayesian network structure learning techniques by thoroughly studying several variants of Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm (HMOBEDA), applied to the MNK Landscape combinatorial problem. In the experiments, we evaluate the performance considering three different aspects: optimization abilities, robustness and learning efficiency. Results for instances of multi- and many-objective MNK-landscape show that, score-based structure learning algorithms appear to be the best choice. In particular, HMOBEDA $$_{k2}$$ k 2 was capable of producing results comparable with the other variants in terms of the runtime of convergence and the coverage of the final Pareto front, with the additional advantage of providing solutions that are less sensible to noise while the variability of the corresponding Bayesian network models is reduced.

Suggested Citation

  • Marcella S. R. Martins & Mohamed El Yafrani & Myriam Delgado & Ricardo Lüders & Roberto Santana & Hugo V. Siqueira & Huseyin G. Akcay & Belaïd Ahiod, 2021. "Analysis of Bayesian Network Learning Techniques for a Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm: a case study on MNK Landscape," Journal of Heuristics, Springer, vol. 27(4), pages 549-573, August.
    • Handle: RePEc:spr:joheur:v:27:y:2021:i:4:d:10.1007_s10732-021-09469-x
    DOI: 10.1007/s10732-021-09469-x
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    References listed on IDEAS

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    1. Aguirre, Hernan E. & Tanaka, Kiyoshi, 2007. "Working principles, behavior, and performance of MOEAs on MNK-landscapes," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1670-1690, September.
    2. Paulo S. G. de Mattos Neto & Manoel H. N. Marinho & Hugo Siqueira & Yara de Souza Tadano & Vivian Machado & Thiago Antonini Alves & João Fausto L. de Oliveira & Francisco Madeiro, 2020. "A Methodology to Increase the Accuracy of Particulate Matter Predictors Based on Time Decomposition," Sustainability, MDPI, vol. 12(18), pages 1-33, September.
    3. Shah, Ruchit & Reed, Patrick, 2011. "Comparative analysis of multiobjective evolutionary algorithms for random and correlated instances of multiobjective d-dimensional knapsack problems," European Journal of Operational Research, Elsevier, vol. 211(3), pages 466-479, June.
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