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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Bibliographic Details
Published in:European journal of operational research Vol. 175; no. 2; pp. 931 - 947
Main Authors: Miettinen, Kaisa, Mäkelä, Marko M., Kaario, Katja
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.12.2006
Elsevier
Elsevier Sequoia S.A
Series:European Journal of Operational Research
Subjects:
Applied sciences
Classification
Exact sciences and technology
Guaranteeing Pareto optimality
Interactive methods
Mathematical programming
Multiple objective programming
Operational research and scientific management
Operational research. Management science
Optimization
Pareto optimum
Studies
Test problems
Interactive methods
Test problems
Guaranteeing Pareto optimality
Multiple objective programming
Classification
Pareto optimum
Interactive mode
Multiobjective programming
Scalarization
Guarantee
Location problem
Optimization
ISSN:0377-2217, 1872-6860, 1872-6860
Online Access:Get full text
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Summary: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.
Bibliography:SourceType-Scholarly Journals-1
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ISSN:0377-2217
1872-6860
1872-6860
DOI:10.1016/j.ejor.2005.06.019