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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