Optimistic NAUTILUS navigator for multiobjective optimization with costly function evaluations
Saini, B. S., Emmerich, M., Mazumdar, A., Afsar, B., Shavazipour, B., & Miettinen, K. (2022). Optimistic NAUTILUS navigator for multiobjective optimization with costly function evaluations. Journal of Global Optimization, 83(4), 865-889. https://doi.org/10.1007/s10898-021-01119-7
Published inJournal of Global Optimization
DisciplineMultiobjective Optimization GroupLaskennallinen tiedeTietojärjestelmätiedeMultiobjective Optimization GroupComputational ScienceInformation Systems Science
© The Author(s) 2021
We introduce novel concepts to solve multiobjective optimization problems involving (computationally) expensive function evaluations and propose a new interactive method called O-NAUTILUS. It combines ideas of trade-off free search and navigation (where a decision maker sees changes in objective function values in real time) and extends the NAUTILUS Navigator method to surrogate-assisted optimization. Importantly, it utilizes uncertainty quantification from surrogate models like Kriging or properties like Lipschitz continuity to approximate a so-called optimistic Pareto optimal set. This enables the decision maker to search in unexplored parts of the Pareto optimal set and requires a small amount of expensive function evaluations. We share the implementation of O-NAUTILUS as open source code. Thanks to its graphical user interface, a decision maker can see in real time how the preferences provided affect the direction of the search. We demonstrate the potential and benefits of O-NAUTILUS with a problem related to the design of vehicles. ...
PublisherSpringer Science and Business Media LLC
ISSN Search the Publication Forum0925-5001
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 fundingThis research was partly funded by the Academy of Finland (Grants 322221 and 311877). The research is related to the thematic research area Decision Analytics utilizing Causal Models and Multiobjective Optimization (DEMO), jyu.fi/demo, at the University of Jyväskylä.
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