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

Marcella S. R. Martins (), Mohamed El Yafrani (), Myriam Delgado (), Ricardo Lüders (), Roberto Santana (), Hugo V. Siqueira (), Huseyin G. Akcay () and Belaïd Ahiod ()
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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

Journal of Heuristics, 2021, vol. 27, issue 4, No 2, 549-573

Abstract: 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.

Keywords: Many-objective optimization; Estimation of distribution algorithms; Structure learning techniques; Robustness (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10732-021-09469-x

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