A Preference-guided Multiobjective Evolutionary Algorithm based on Decomposition

Daniel Edilson de Souza, Fillipe Goulart, Lucas S. Batista, Felipe Campelo*

*Corresponding author for this work

    Research output: Chapter in Book/Published conference outputConference publication

    Abstract

    Multiobjective evolutionary algorithms based on decomposition
    (MOEA/Ds) represent a class of widely employed problem solvers for multicriteria optimization problems. In this work we investigate the adaptation of
    these methods for incorporating preference information prior to the optimization, so that the search process can be biased towards a Pareto-optimal region
    that better satisfies the aspirations of a decision-making entity. The incorporation of the Preference-based Adaptive Region-of-interest (PAR) framework into
    the MOEA/D requires only the modification of the reference points used within
    the scalarization function, which in principle allows a straightforward use in
    more sophisticated versions of the base algorithm. Experimental results using
    the UF benchmark set suggest gains in diversity within the region of interest,
    without significant losses in convergence.
    Original languageEnglish
    Title of host publicationXIV Encontro Nacional de Inteligencia Artificial e Computacional
    Pages37-48
    Publication statusPublished - 5 Oct 2017
    EventXIV Encontro Nacional de Inteligência Artificial e Computacional - Uberlândia, Brazil
    Duration: 2 Oct 20175 Oct 2017

    Conference

    ConferenceXIV Encontro Nacional de Inteligência Artificial e Computacional
    Country/TerritoryBrazil
    CityUberlândia
    Period2/10/175/10/17

    Bibliographical note

    © 2017 The Authors

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