TY - JOUR
T1 - RDS-NSGA-II
T2 - a memetic algorithm for reference point based multi-objective optimization
AU - Hernández Mejía, Jesus Alejandro
AU - Schütze, Oliver
AU - Cuate, Oliver
AU - Lara, Adriana
AU - Deb, Kalyanmoy
N1 - Publisher Copyright:
© 2016 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2017/5/4
Y1 - 2017/5/4
N2 - Reference point based optimization offers tools for the effective treatment of preference based multi-objective optimization problems, e.g. when the decision-maker has a rough idea about the target objective values. For the numerical solution of such problems, specialized evolutionary strategies have become popular, despite their possible slow convergence rates. Hybridizing such evolutionary algorithms with local search techniques have been shown to produce faster and more reliable algorithms. In this article, the directed search (DS) method is adapted to the context of reference point optimization problems, making this variant, called RDS, a well-suited option for integration into evolutionary algorithms. Numerical results on academic test problems with up to five objectives demonstrate the benefit of the novel hybrid (i.e. the same approximation quality can be obtained more efficiently by the new algorithm), using the state-of-the-art algorithm R-NSGA-II for this coupling. This represents an advantage when treating costly-to-evaluate real-world engineering design problems.
AB - Reference point based optimization offers tools for the effective treatment of preference based multi-objective optimization problems, e.g. when the decision-maker has a rough idea about the target objective values. For the numerical solution of such problems, specialized evolutionary strategies have become popular, despite their possible slow convergence rates. Hybridizing such evolutionary algorithms with local search techniques have been shown to produce faster and more reliable algorithms. In this article, the directed search (DS) method is adapted to the context of reference point optimization problems, making this variant, called RDS, a well-suited option for integration into evolutionary algorithms. Numerical results on academic test problems with up to five objectives demonstrate the benefit of the novel hybrid (i.e. the same approximation quality can be obtained more efficiently by the new algorithm), using the state-of-the-art algorithm R-NSGA-II for this coupling. This represents an advantage when treating costly-to-evaluate real-world engineering design problems.
KW - Multi-objective optimization
KW - memetic strategy
KW - reference point problem
UR - http://www.scopus.com/inward/record.url?scp=84981722625&partnerID=8YFLogxK
U2 - 10.1080/0305215X.2016.1211127
DO - 10.1080/0305215X.2016.1211127
M3 - Artículo
SN - 0305-215X
VL - 49
SP - 828
EP - 845
JO - Engineering Optimization
JF - Engineering Optimization
IS - 5
ER -