TY - JOUR
T1 - A scalar optimization approach for averaged Hausdorff approximations of the Pareto front
AU - Schütze, Oliver
AU - Domínguez-Medina, Christian
AU - Cruz-Cortés, Nareli
AU - Gerardo de la Fraga, Luis
AU - Sun, Jian Qiao
AU - Toscano, Gregorio
AU - Landa, Ricardo
N1 - Publisher Copyright:
© 2016 Taylor & Francis.
PY - 2016/9/1
Y1 - 2016/9/1
N2 - This article presents a novel method to compute averaged Hausdorff (Δp) approximations of the Pareto fronts of multi-objective optimization problems. The underlying idea is to utilize directly the scalar optimization problem that is induced by the Δp performance indicator. This method can be viewed as a certain set based scalarization approach and can be addressed both by mathematical programming techniques and evolutionary algorithms (EAs). In this work, the focus is on the latter where a first single objective EA for such Δp approximations is proposed. Finally, the strength of the novel approach is demonstrated on some bi-objective benchmark problems with different shapes of the Pareto front.
AB - This article presents a novel method to compute averaged Hausdorff (Δp) approximations of the Pareto fronts of multi-objective optimization problems. The underlying idea is to utilize directly the scalar optimization problem that is induced by the Δp performance indicator. This method can be viewed as a certain set based scalarization approach and can be addressed both by mathematical programming techniques and evolutionary algorithms (EAs). In this work, the focus is on the latter where a first single objective EA for such Δp approximations is proposed. Finally, the strength of the novel approach is demonstrated on some bi-objective benchmark problems with different shapes of the Pareto front.
KW - evolutionary computation
KW - indicator
KW - multi-objective optimization
KW - scalarization
UR - http://www.scopus.com/inward/record.url?scp=84955095514&partnerID=8YFLogxK
U2 - 10.1080/0305215X.2015.1124872
DO - 10.1080/0305215X.2015.1124872
M3 - Artículo
SN - 0305-215X
VL - 48
SP - 1593
EP - 1617
JO - Engineering Optimization
JF - Engineering Optimization
IS - 9
ER -