Experiments with classification-based scalarizing functions in interactive multiobjective optimization

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...

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Vydáno v:European journal of operational research Ročník 175; číslo 2; s. 931 - 947
Hlavní autoři: Miettinen, Kaisa, Mäkelä, Marko M., Kaario, Katja
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 01.12.2006
Elsevier
Elsevier Sequoia S.A
Edice:European Journal of Operational Research
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ISSN:0377-2217, 1872-6860, 1872-6860
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Shrnutí: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.
Bibliografie:SourceType-Scholarly Journals-1
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ISSN:0377-2217
1872-6860
1872-6860
DOI:10.1016/j.ejor.2005.06.019