A1 Journal article (refereed)
An Approach to the Automatic Comparison of Reference Point-Based Interactive Methods for Multiobjective Optimization (2021)

Podkopaev, D., Miettinen, K., & Ojalehto, V. (2021). An Approach to the Automatic Comparison of Reference Point-Based Interactive Methods for Multiobjective Optimization. IEEE Access, 9, 150037-150048. https://doi.org/10.1109/access.2021.3123432

JYU authors or editors

Publication details

All authors or editors: Podkopaev, Dmitry; Miettinen, Kaisa; Ojalehto, Vesa

Journal or series: IEEE Access

eISSN: 2169-3536

Publication year: 2021

Volume: 9

Pages range: 150037-150048

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1109/access.2021.3123432

Publication open access: Openly available

Publication channel open access: Open Access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/78488

Abstract

Solving multiobjective optimization problems means finding the best balance among multiple conflicting objectives. This needs preference information from a decision maker who is a domain expert. In interactive methods, the decision maker takes part in an iterative process to learn about the interdependencies and can adjust the preferences. We address the need to compare different interactive multiobjective optimization methods, which is essential when selecting the most suited method for solving a particular problem. We concentrate on a class of interactive methods where a decision maker expresses preference information as reference points, i.e., desirable objective function values. Comparison of interactive methods with human decision makers is not a straightforward process due to cost and reliability issues. The lack of suitable behavioral models hampers creating artificial decision makers for automatic experiments. Few approaches to automating testing have been proposed in the literature; however, none are widely used. As a result, empirical performance studies are scarce for this class of methods despite its popularity among researchers and practitioners.We have developed a new approach to replace a decision maker to automatically compare interactive methods based on reference points or similar preference information. Keeping in mind the lack of suitable human behavioral models, we concentrate on evaluating general performance characteristics. Such an evaluation can partly address the absence of any tests and is appropriate for screening methods before more rigorous testing. We have implemented our approach as a ready-to-use Python module and illustrated it with computational examples.

Keywords: decision support systems; optimisation; multi-objective optimisation; interactivity; testing

Free keywords: decision making; interactive systems; multiobjective optimization; optimization; optimization methods; testing

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2021

JUFO rating: 2