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Hybrid multi-objective Bayesian estimation of distribution algorithm: a comparative analysis for the multi-objective knapsack problem

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  • Marcella S. R. Martins

    (Federal University of Technology - Paraná (UTFPR))

  • Myriam R. B. S. Delgado

    (Federal University of Technology - Paraná (UTFPR))

  • Ricardo Lüders

    (Federal University of Technology - Paraná (UTFPR))

  • Roberto Santana

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

  • Richard A. Gonçalves

    (Midwest State University of Parana (UNICENTRO))

  • Carolina P. Almeida

    (Midwest State University of Parana (UNICENTRO))

Abstract

Nowadays, a number of metaheuristics have been developed for efficiently solving multi-objective optimization problems. Estimation of distribution algorithms are a special class of metaheuristic that intensively apply probabilistic modeling and, as well as local search methods, are widely used to make the search more efficient. In this paper, we apply a Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm (HMOBEDA) in multi and many objective scenarios by modeling the joint probability of decision variables, objectives, and the configuration parameters of an embedded local search (LS). We analyze the benefits of the online configuration of LS parameters by comparing the proposed approach with LS off-line versions using instances of the multi-objective knapsack problem with two to five and eight objectives. HMOBEDA is also compared with five advanced evolutionary methods using the same instances. Results show that HMOBEDA outperforms the other approaches including those with off-line configuration. HMOBEDA not only provides the best value for hypervolume indicator and IGD metric in most of the cases, but it also computes a very diverse solutions set close to the estimated Pareto front.

Suggested Citation

  • Marcella S. R. Martins & Myriam R. B. S. Delgado & Ricardo Lüders & Roberto Santana & Richard A. Gonçalves & Carolina P. Almeida, 2018. "Hybrid multi-objective Bayesian estimation of distribution algorithm: a comparative analysis for the multi-objective knapsack problem," Journal of Heuristics, Springer, vol. 24(1), pages 25-47, February.
  • Handle: RePEc:spr:joheur:v:24:y:2018:i:1:d:10.1007_s10732-017-9356-7
    DOI: 10.1007/s10732-017-9356-7
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    References listed on IDEAS

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    1. 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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