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Journal of Heuristics

, Volume 24, Issue 1, pp 25–47 | Cite as

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

  • Marcella S. R. Martins
  • Myriam R. B. S. Delgado
  • Ricardo Lüders
  • Roberto Santana
  • Richard A. Gonçalves
  • Carolina P. de Almeida
Article
  • 199 Downloads

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.

Keywords

Multi-objective estimation of distribution algorithms Probabilistic modeling Local search Hybridization Automatic algorithm configuration 

Notes

Acknowledgements

M. Delgado acknowledges CNPq Grant 309197/2014-7. M. Martins acknowledges CAPES/Brazil. R. Santana acknowledges support by: IT-609-13 Program (Basque Government), TIN2013-41272P (Spanish Ministry of Science and Innovation) and CNPq Program Science Without Borders No. 400125/2014-5 (Brazil Government).

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Federal University of Technology - Paraná (UTFPR)CuritibaBrazil
  2. 2.University of the Basque Country (UPV/EHU)Donostia, San SebastiánSpain
  3. 3.Midwest State University of Parana (UNICENTRO)GuarapuavaBrazil

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