Comparing interactive evolutionary multiobjective optimization methods with an artificial decision maker
Afsar, B., Ruiz, A. B., & Miettinen, K. (2021). Comparing interactive evolutionary multiobjective optimization methods with an artificial decision maker. Complex & Intelligent systems, Early online. https://doi.org/10.1007/s40747-021-00586-5
Published inComplex & Intelligent systems
DisciplineLaskennallinen tiedeMultiobjective Optimization GroupComputational ScienceMultiobjective Optimization Group
© 2021 the Authors
Solving multiobjective optimization problems with interactive methods enables a decision maker with domain expertise to direct the search for the most preferred trade-offs with preference information and learn about the problem. There are different interactive methods, and it is important to compare them and find the best-suited one for solving the problem in question. Comparisons with real decision makers are expensive, and artificial decision makers (ADMs) have been proposed to simulate humans in basic testing before involving real decision makers. Existing ADMs only consider one type of preference information. In this paper, we propose ADM-II, which is tailored to assess several interactive evolutionary methods and is able to handle different types of preference information. We consider two phases of interactive solution processes, i.e., learning and decision phases separately, so that the proposed ADM-II generates preference information in different ways in each of them to reflect the nature of the phases. We demonstrate how ADM-II can be applied with different methods and problems. We also propose an indicator to assess and compare the performance of interactive evolutionary methods. ...
PublisherSpringer Science+Business Media
ISSN Search the Publication Forum2199-4536
Publication in research information system
MetadataShow full item record
Related funder(s)Academy of Finland
Funding program(s)Academy Project, AoF; Research profiles, AoF
Additional information about fundingThe authors would like to thank the financial support received from the Spanish government (Grant ECO2017-88883-R), the regional government of Andalusia (Grant UMA18-FEDERJA-024 and PAI group SEJ-532), and the Academy of Finland (Grants 322221 and 311877).
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