Publications · 2016 · Journal article
A Scalar Optimization Approach for Averaged Hausdorff Approximations of the Pareto Front
Engineering Optimization, 48(9): 1593–1617, 2016
Abstract
This article presents a novel method to compute averaged Hausdorff 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 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 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.
BibTeX
@article{Schutze16a,
author = {Oliver Schutze and Christian Dominguez-Medina and Nareli Cruz-Cortes and Luis Gerardo {de la Fraga} and Jian-Qiao Sun and Gregorio Toscano and Ricardo Landa},
title = {{A Scalar Optimization Approach for Averaged Hausdorff Approximations of the Pareto Front}},
journal = {Engineering Optimization},
volume = {48},
number = {9},
pages = {1593--1617},
year = {2016},
doi = {10.1080/0305215X.2015.1124872},
url = {http://dx.doi.org/10.1080/0305215X.2015.1124872},
}