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


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Ministry reporting: Yes

Reporting Year: 2020

JUFO rating: 1


Last updated on 2021-09-08 at 11:07