A scalar optimization approach for averaged Hausdorff approximations of the Pareto front. Issue 9 (1st September 2016) - Schütze, Oliver; Domínguez-Medina, Christian; Cruz-Cortés, Nareli; Gerardo de la Fraga, Luis; Sun, Jian-Qiao; Toscano, Gregorio; Landa, Ricardo
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.