Many-Objective Quality Measures
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Afsar, B., Fieldsend, J. E., Guerreiro, A. P., Miettinen, K., Rojas Gonzalez, S., & Sato, H. (2023). Many-Objective Quality Measures. In D. Brockhoff, M. Emmerich, B. Naujoks, & R. Purshouse (Eds.), Many-Criteria Optimization and Decision Analysis : State-of-the-Art, Present Challenges, and Future Perspective (pp. 113-148). Springer. Natural Computing Series. https://doi.org/10.1007/978-3-031-25263-1_5
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Natural Computing SeriesDate
2023Discipline
Laskennallinen tiedeMultiobjective Optimization GroupPäätöksen teko monitavoitteisestiComputational ScienceMultiobjective Optimization GroupDecision analytics utilizing causal models and multiobjective optimizationAccess restrictions
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© 2023 Springer Nature Switzerland AG
A key concern when undertaking any form of optimisation is how to characterise the quality of the putative solution returned. In many-objective optimisation an added complication is that such measures are on a set of trade-off solutions. We present and discuss the commonly used quality measures for many-objective optimisation, which are a subset of those used in multi-objective optimisation. We discuss the computational aspects and theoretical properties of these measures, highlighting measures for both a posteriori and a priori approaches, where the latter incorporate preference information from a decision maker (DM). We also discuss open areas in this field and forms of many-objective optimisation which are relatively under-explored, and where appropriate quality measures are much less developed including challenges related to developing measures for interactive methods.
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Many-Criteria Optimization and Decision Analysis : State-of-the-Art, Present Challenges, and Future PerspectiveISSN Search the Publication Forum
1619-7127Keywords
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https://converis.jyu.fi/converis/portal/detail/Publication/184269258
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This work was initiated during the MACODA: Many Criteria Optimisation and Decision Analysis Workshop at the Lorentz Center (Leiden, The Netherlands), 2019. We are grateful to the other participants of the workshop and the Lorentz Center for their support. Jonathan E. Fieldsend was supported in attending the MACODA workshop by Innovate UK [grant number 104400]. Bekir Afsar’s research was funded by ...
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