A4 Article in conference proceedings
An Interactive Framework for Offline Data-Driven Multiobjective Optimization (2020)
Mazumdar, A., Chugh, T., Hakanen, J., & Miettinen, K. (2020). An Interactive Framework for Offline Data-Driven Multiobjective Optimization. In B. Filipic, E. Minisci, & M. Vasilei (Eds.), BIOMA 2020 : 9th International Conference on Bioinspired Optimization Methods and Their Applications, Proceedings (pp. 97-109). Springer. Lecture Notes in Computer Science, 12438. https://doi.org/10.1007/978-3-030-63710-1_8
JYU authors or editors
Publication details
All authors or editors: Mazumdar, Atanu; Chugh, Tinkle; Hakanen, Jussi; Miettinen, Kaisa
Parent publication: BIOMA 2020 : 9th International Conference on Bioinspired Optimization Methods and Their Applications, Proceedings
Parent publication editors: Filipic, Bogdan; Minisci, Edmondo; Vasilei, Massimiliano
Conference:
- International conference on bioinspired optimization methods and their applications
Place and date of conference: Brussels, Belgium, 19.-20.11.2020
ISBN: 978-3-030-63709-5
eISBN: 978-3-030-63710-1
Journal or series: Lecture Notes in Computer Science
ISSN: 0302-9743
eISSN: 1611-3349
Publication year: 2020
Number in series: 12438
Pages range: 97-109
Number of pages in the book: 322
Publisher: Springer
Place of Publication: Cham
Publication country: Switzerland
Publication language: English
DOI: https://doi.org/10.1007/978-3-030-63710-1_8
Publication open access: Not open
Publication channel open access:
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/72822
Abstract
We propose a framework for solving offline data-driven multiobjective optimization problems in an interactive manner. No new data becomes available when solving offline problems. We fit surrogate models to the data to enable optimization, which introduces uncertainty. The framework incorporates preference information from a decision maker in two aspects to direct the solution process. Firstly, the decision maker can guide the optimization by providing preferences for objectives. Secondly, the framework features a novel technique for the decision maker to also express preferences related to maximum acceptable uncertainty in the solutions as preferred ranges of uncertainty. In this way, the decision maker can understand what uncertainty in solutions means and utilize this information for better decision making. We aim at keeping the cognitive load on the decision maker low and propose an interactive visualization that enables the decision maker to make decisions based on uncertainty. The interactive framework utilizes decomposition-based multiobjective evolutionary algorithms and can be extended to handle different types of preferences for objectives. Finally, we demonstrate the framework by solving a practical optimization problem with ten objectives.
Keywords: multi-objective optimisation; decision support systems; kriging method; Gaussian processes
Free keywords: decision support; decision making; decomposition-based MOEA; metamodelling; surrogate; Kriging; Gaussian processes
Contributing organizations
Related projects
- Competitive funding to strengthen universities’ research profiles. Profiling actions at the JYU, round 3
- Hämäläinen, Keijo
- Research Council of Finland
Ministry reporting: Yes
Reporting Year: 2020
JUFO rating: 1