A4 Artikkeli konferenssijulkaisussa
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-tekijät tai -toimittajat

Mazumdar, Atanu
Hakanen, Jussi
Miettinen, Kaisa

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Mazumdar, Atanu; Chugh, Tinkle; Hakanen, Jussi; Miettinen, Kaisa

Emojulkaisu: BIOMA 2020 : 9th International Conference on Bioinspired Optimization Methods and Their Applications, Proceedings

Emojulkaisun toimittajat: Filipic, Bogdan; Minisci, Edmondo; Vasilei, Massimiliano

Konferenssi:

International conference on bioinspired optimization methods and their applications

Konferenssin paikka ja aika: Brussels, Belgium, 19.-20.11.2020

ISBN: 978-3-030-63709-5

eISBN: 978-3-030-63710-1

Lehti tai sarja: Lecture Notes in Computer Science

ISSN: 0302-9743

eISSN: 1611-3349

Julkaisuvuosi: 2020

Sarjan numero: 12438

Artikkelin sivunumerot: 97-109

Kirjan kokonaissivumäärä: 322

Kustantaja: Springer

Kustannuspaikka: Cham

Julkaisumaa: Sveitsi

Julkaisun kieli: englanti

DOI: https://doi.org/10.1007/978-3-030-63710-1_8

Julkaisun avoin saatavuus: Ei avoin

Julkaisukanavan avoin saatavuus:

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/72822

Tiivistelmä

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.

YSO-asiasanat: monitavoiteoptimointi; päätöksentukijärjestelmät; kriging-menetelmä; gaussiset prosessit

Vapaat asiasanat: decision support; decision making; decomposition-based MOEA; metamodelling; surrogate; Kriging; Gaussian processes

Tieteenalat: