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Experiments with classification-based scalarizing functions in interactive multiobjective optimization
Helsinki School of Economics.
Department of Mathematical Information Technology, University of Jyväskylä.
Department of Mathematical Information Technology, University of Jyväskylä.
2006 (English)In: European Journal of Operational Research, ISSN 0377-2217, E-ISSN 1872-6860, Vol. 175, no 2, 931-947 p.Article in journal (Refereed) Published
Abstract [en]

In multiobjective optimization methods, the multiple conflicting objectives are typically converted into a single objective optimization problem with the help of scalarizing functions and such functions may be constructed in many ways. We compare both theoretically and numerically the performance of three classification-based scalarizing functions and pay attention to how well they obey the classification information. In particular, we devote special interest to the differences the scalarizing functions have in the computational cost of guaranteeing Pareto optimality. It turns out that scalarizing functions with or without so-called augmentation terms have significant differences in this respect. We also collect a set of mostly nonlinear benchmark test problems that we use in the numerical comparisons.

Place, publisher, year, edition, pages
2006. Vol. 175, no 2, 931-947 p.
Keyword [en]
Multiple objective programming, Classification, Interactive methods, Test problems, Guaranteeing Pareto optimality
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-83672DOI: 10.1016/j.ejor.2005.06.019ISI: 000241063500018OAI: oai:DiVA.org:kth-83672DiVA: diva2:498897
Note
QC 20120227Available from: 2012-02-12 Created: 2012-02-12 Last updated: 2017-12-07Bibliographically approved

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