A4 Article in conference proceedings
An Artificial Decision Maker for Comparing Reference Point Based Interactive Evolutionary Multiobjective Optimization Methods (2021)


Afsar, B., Miettinen, K., & Ruiz, A. B. (2021). An Artificial Decision Maker for Comparing Reference Point Based Interactive Evolutionary Multiobjective Optimization Methods. In H. Ishibuchi, Q. Zhang, R. Cheng, K. Li, H. Li, H. Wang, & A. Zhou (Eds.), Evolutionary Multi-Criterion Optimization : 11th International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings (pp. 619-631). Springer. Lecture notes in computer science, 12654. https://doi.org/10.1007/978-3-030-72062-9_49


JYU authors or editors


Publication details

All authors or editors: Afsar, Bekir; Miettinen, Kaisa; Ruiz, Ana B.

Parent publication: Evolutionary Multi-Criterion Optimization : 11th International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings

Parent publication editors: Ishibuchi, Hisao; Zhang, Qingfu; Cheng, Ran; Li, Ke; Li, Hui; Wang, Handing; Zhou, Aimin

Place and date of conference: Shenzhen, China, 28-31.3.2021

ISBN: 978-3-030-72061-2

eISBN: 978-3-030-72062-9

Journal or series: Lecture notes in computer science

ISSN: 0302-9743

eISSN: 1611-3349

Publication year: 2021

Number in series: 12654

Pages range: 619-631

Publisher: Springer

Place of Publication: Cham

Publication country: Switzerland

Publication language: English

DOI: https://doi.org/10.1007/978-3-030-72062-9_49

Publication open access: Not open

Publication channel open access:

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/74854


Abstract

Comparing interactive evolutionary multiobjective optimization methods is controversial. The main difficulties come from features inherent to interactive solution processes involving real decision makers. The human can be replaced by an artificial decision maker (ADM) to evaluate methods quantitatively. We propose a new ADM to compare reference point based interactive evolutionary methods, where reference points are generated in different ways for the different phases of the solution process. In the learning phase, the ADM explores different parts of the objective space to gain insight about the problem and to identify a region of interest, which is studied more closely in the decision phase. We demonstrate the ADM by comparing interactive versions of RVEA and NSGA-III on benchmark problems with up to 9 objectives. The experiments show that our ADM is efficient and allows repetitive testing to compare interactive evolutionary methods in a meaningful way.


Keywords: decision making; optimisation; multi-objective optimisation; multivariable methods

Free keywords: decision making; aspiration levels; performance comparison; many-objective optimization; interactive methods


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Ministry reporting: Yes

Reporting Year: 2021

Preliminary JUFO rating: 1


Last updated on 2021-08-12 at 08:32