Designing empirical experiments to compare interactive multiobjective optimization methods
Afsar, B., Silvennoinen, J., Misitano, G., Ruiz, F., Ruiz, A. B., & Miettinen, K. (2022). Designing empirical experiments to compare interactive multiobjective optimization methods. Journal of the operational research society, Early online. https://doi.org/10.1080/01605682.2022.2141145
Published inJournal of the operational research society
DisciplineKoulutusteknologia ja kognitiotiedeMultiobjective Optimization GroupLaskennallinen tiedeResurssiviisausyhteisöKognitiotiedeLearning and Cognitive SciencesMultiobjective Optimization GroupComputational ScienceSchool of Resource WisdomCognitive Science
© 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
Interactive multiobjective optimization methods operate iteratively so that a decision maker directs the solution process by providing preference information, and only solutions of interest are generated. These methods limit the amount of information considered in each iteration and support the decision maker in learning about the trade-offs. Many interactive methods have been developed, and they differ in technical aspects and the type of preference information used. Finding the most appropriate method for a problem to be solved is challenging, and supporting the selection is crucial. Published research lacks information on the conducted experiments’ specifics (e.g. questions asked), making it impossible to replicate them. We discuss the challenges of conducting experiments and offer realistic means to compare interactive methods. We propose a novel questionnaire and experimental design and, as proof of concept, apply them in comparing two methods. We also develop user interfaces for these methods and introduce a sustainability problem with multiple objectives. The proposed experimental setup is reusable, enabling further experiments. ...
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Related funder(s)Academy of Finland
Funding program(s)Research profiles, AoF; Academy Project, AoF
Additional information about fundingThis research was partly supported by the Academy of Finland (grants 311877 and 322221) and the Vilho, Yrjö and Kalle Väisälä Foundation (grant 200033) and is related to the thematic research area DEMO (Decision Analytics utilizing Causal Models and Multiobjective Optimization, jyu.fi/demo) at the University of Jyvaskyla; the Spanish Ministry of Science (projects PID2019-104263RB-C42 and PID2020-115429GB-I00); the Regional Government of Andalucía (projects SEJ-532, P18-RT-1566 and UMA18-FEDERJA-065); and the University of Málaga (grant B1-2020-18). ...
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