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.
(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 language | English |
|---|---|
| Title of host publication | XIV Encontro Nacional de Inteligencia Artificial e Computacional |
| Publication status | Published - 5 Oct 2017 |