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Central European Journal of Operations Research

, Volume 25, Issue 4, pp 859–878 | Cite as

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

  • Ernestas Filatovas
  • Algirdas Lančinskas
  • Olga Kurasova
  • Julius Žilinskas
Original Paper

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.

Keywords

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

Notes

Acknowledgments

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

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Ernestas Filatovas
    • 1
  • Algirdas Lančinskas
    • 1
  • Olga Kurasova
    • 1
  • Julius Žilinskas
    • 1
  1. 1.Institute of Mathematics and InformaticsVilnius UniversityVilniusLithuania

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