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


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.

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This research is funded by a Grant (No. MIP-051/2014) from the Research Council of Lithuania.

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Correspondence to Ernestas Filatovas.

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Filatovas, E., Lančinskas, A., Kurasova, O. et al. A preference-based multi-objective evolutionary algorithm R-NSGA-II with stochastic local search. Cent Eur J Oper Res 25, 859–878 (2017).

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  • Multi-objective optimization
  • Preference-based evolutionary algorithms
  • Memetic algorithm
  • Stochastic local search