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
Contributing organizations
Related projects
- Competitive funding to strengthen universities’ research profiles. Profiling actions at the JYU, round 3
- Hämäläinen, Keijo
- Research Council of Finland
- Data-driven Decision Support with Multiobjective Optimization (DAEMON)
- Miettinen, Kaisa
- Research Council of Finland
Ministry reporting: Yes
Reporting Year: 2021
JUFO rating: 1