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A preference-based multi-objective evolutionary algorithm R-NSGA-II with stochastic local search

Author

Listed:
  • Ernestas Filatovas

    (Vilnius University)

  • Algirdas Lančinskas

    (Vilnius University)

  • Olga Kurasova

    (Vilnius University)

  • Julius Žilinskas

    (Vilnius University)

Abstract

Incorporation of a decision maker’s preferences into multi-objective evolutionary algorithms has become a relevant trend during the last decade, and several preference-based evolutionary algorithms have been proposed in the literature. Our research is focused on improvement of a well-known preference-based evolutionary algorithm R-NSGA-II by incorporating a local search strategy based on a single agent stochastic approach. The proposed memetic algorithm has been experimentally evaluated by solving a set of well-known multi-objective optimization benchmark problems. It has been experimentally shown that incorporation of the local search strategy has a positive impact to the quality of the algorithm in the sense of the precision and distribution evenness of approximation.

Suggested Citation

  • Ernestas Filatovas & Algirdas Lančinskas & Olga Kurasova & Julius Žilinskas, 2017. "A preference-based multi-objective evolutionary algorithm R-NSGA-II with stochastic local search," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 25(4), pages 859-878, December.
    • Handle: RePEc:spr:cejnor:v:25:y:2017:i:4:d:10.1007_s10100-016-0443-x
    DOI: 10.1007/s10100-016-0443-x
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    References listed on IDEAS

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    1. Ana Ruiz & Rubén Saborido & Mariano Luque, 2015. "A preference-based evolutionary algorithm for multiobjective optimization: the weighting achievement scalarizing function genetic algorithm," Journal of Global Optimization, Springer, vol. 62(1), pages 101-129, May.
    2. Francisco J. Solis & Roger J.-B. Wets, 1981. "Minimization by Random Search Techniques," Mathematics of Operations Research, INFORMS, vol. 6(1), pages 19-30, February.
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    Cited by:

    1. Tunjo Perić & Zoran Babić & Josip Matejaš, 2018. "Comparative analysis of application efficiency of two iterative multi objective linear programming methods (MP method and STEM method)," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(3), pages 565-583, September.
    2. E. Filatovas & O. Kurasova & J. L. Redondo & J. Fernández, 2020. "A reference point-based evolutionary algorithm for approximating regions of interest in multiobjective problems," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 402-423, July.
    3. Josip Matejaš & Tunjo Perić & Danijel Mlinarić, 2021. "Which efficient solution in multi objective programming problem should be taken?," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(3), pages 967-987, September.

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