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
Julkaistu sarjassa
Journal of Global OptimizationTekijät
Päivämäärä
2022Oppiaine
Multiobjective Optimization GroupLaskennallinen tiedeTietojärjestelmätiedePäätöksen teko monitavoitteisestiMultiobjective Optimization GroupComputational ScienceInformation Systems ScienceDecision analytics utilizing causal models and multiobjective optimizationTekijänoikeudet
© 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.
...
Julkaisija
Springer Science and Business Media LLCISSN Hae Julkaisufoorumista
0925-5001Asiasanat
Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/103601972
Metadata
Näytä kaikki kuvailutiedotKokoelmat
Rahoittaja(t)
Suomen AkatemiaRahoitusohjelmat(t)
Akatemiahanke, SA; Profilointi, SALisätietoja rahoituksesta
This 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.Lisenssi
Samankaltainen aineisto
Näytetään aineistoja, joilla on samankaltainen nimeke tai asiasanat.
-
Assessing the Performance of Interactive Multiobjective Optimization Methods : A Survey
Afsar, Bekir; Miettinen, Kaisa; Ruiz, Francisco (Association for Computing Machinery (ACM), 2021)Interactive methods are useful decision-making tools for multiobjective optimization problems, because they allow a decision-maker to provide her/his preference information iteratively in a comfortable way at the same time ... -
Flexible data driven inventory management with interactive multiobjective lot size optimization
Heikkinen, Risto; Sipilä, Juha; Ojalehto, Vesa; Miettinen, Kaisa (Inderscience Publishers, 2023)We study data-driven decision support and formalise a path from data to decision making. We focus on lot sizing in inventory management with stochastic demand and propose an interactive multi-objective optimisation approach. ... -
A New Paradigm in Interactive Evolutionary Multiobjective Optimization
Saini, Bhupinder Singh; Hakanen, Jussi; Miettinen, Kaisa (Springer, 2020)Over the years, scalarization functions have been used to solve multiobjective optimization problems by converting them to one or more single objective optimization problem(s). This study proposes a novel idea of solving ... -
DESDEO: The Modular and Open Source Framework for Interactive Multiobjective Optimization
Misitano, Giovanni; Saini, Bhupinder Singh; Afsar, Bekir; Shavazipour, Babooshka; Miettinen Kaisa (Institute of Electrical and Electronics Engineers (IEEE), 2021)Interactive multiobjective optimization methods incorporate preferences from a human decision maker in the optimization process iteratively. This allows the decision maker to focus on a subset of solutions, learn about the ... -
Decision making in multiobjective optimization problems under uncertainty : balancing between robustness and quality
Zhou-Kangas, Yue; Miettinen, Kaisa (Springer, 2019)As an emerging research field, multiobjective robust optimization employs minmax robustness as the most commonly used concept. Light robustness is a concept in which a parameter, tolerable degradations, can be used to ...
Ellei toisin mainittu, julkisesti saatavilla olevia JYX-metatietoja (poislukien tiivistelmät) saa vapaasti uudelleenkäyttää CC0-lisenssillä.