A scalar optimization approach for averaged Hausdorff approximations of the Pareto front. Issue 9 (1st September 2016)

• Record Type: Journal Article
• Authors: Schütze, Oliver; Domínguez-Medina, Christian; Cruz-Cortés, Nareli; Gerardo de la Fraga, Luis; Sun, Jian-Qiao; Toscano, Gregorio; Landa, Ricardo
• Is Part Of: Engineering optimization. Volume 48:Issue 9(2016)
• Page Start: 1593
• Page End: 1617
• Publication Date: 2016-09-01
• Subjects: multi-objective optimization -- evolutionary computation -- scalarization -- indicator; Engineering design -- Periodicals; Mathematical optimization -- Periodicals; 620.0042
• DOI: 10.1080/0305215X.2015.1124872
• Languages: English
• ISSNs: 0305-215X

Abstract

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.