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

Ernestas Filatovas (), Algirdas Lančinskas, Olga Kurasova and Julius Žilinskas
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Ernestas Filatovas: Vilnius University
Algirdas Lančinskas: Vilnius University
Olga Kurasova: Vilnius University
Julius Žilinskas: Vilnius University

Central European Journal of Operations Research, 2017, vol. 25, issue 4, No 7, 859-878

Abstract: 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 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10100-016-0443-x

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