A Preference-guided Multiobjective Evolutionary Algorithm based on Decomposition


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.

Divisions: College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Computer Science
Additional Information: © 2017 The Authors
Event Title: XIV Encontro Nacional de Inteligência Artificial e Computacional
Event Type: Other
Event Dates: 2017-10-02 - 2017-10-05
Full Text Link:
Related URLs: http://comissoe ... -ENIAC-2017.pdf (Publisher URL)
PURE Output Type: Conference contribution
Published Date: 2017-10-05
Accepted Date: 2017-01-01
Authors: de Souza, Daniel Edilson
Goulart, Fillipe
Batista, Lucas S.
Campelo, Felipe (ORCID Profile 0000-0001-8432-4325)



Version: Accepted Version

| Preview

Export / Share Citation


Additional statistics for this record