A4 Article in conference proceedings
A New Paradigm in Interactive Evolutionary Multiobjective Optimization (2020)
Saini, B. S., Hakanen, J., & Miettinen, K. (2020). A New Paradigm in Interactive Evolutionary Multiobjective Optimization. In T. Bäck, M. Preuss, A. Deutz, H. Wang, C. Doerr, M. Emmerich, & H. Trautmann (Eds.), PPSN 2020 : 16th International Conference on Parallel Problem Solving from Nature (pp. 243-256). Springer. Lecture Notes in Computer Science, 12270. https://doi.org/10.1007/978-3-030-58115-2_17
JYU authors or editors
Publication details
All authors or editors: Saini, Bhupinder Singh; Hakanen, Jussi; Miettinen, Kaisa
Parent publication: PPSN 2020 : 16th International Conference on Parallel Problem Solving from Nature
Parent publication editors: Bäck, Thomas; Preuss, Mike; Deutz, André; Wang, Hao; Doerr, Carola; Emmerich, Michael; Trautmann, Heike
Place and date of conference: Leiden, The Netherlands, 5.-9.9.2020
ISBN: 978-3-030-58114-5
eISBN: 978-3-030-58115-2
Journal or series: Lecture Notes in Computer Science
ISSN: 0302-9743
eISSN: 1611-3349
Publication year: 2020
Number in series: 12270
Pages range: 243-256
Number of pages in the book: 717
Publisher: Springer
Place of Publication: Cham
Publication country: Switzerland
Publication language: English
DOI: https://doi.org/10.1007/978-3-030-58115-2_17
Publication open access: Not open
Publication channel open access:
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/71637
Abstract
Over the years, scalarization functions have been used to solve multiobjective optimization problems by converting them to one or more single objective optimization problem(s). This study proposes a novel idea of solving multiobjective optimization problems in an interactive manner by using multiple scalarization functions to map vectors in the objective space to a new, so-called preference incorporated space (PIS). In this way, the original problem is converted into a new multiobjective optimization problem with typically fewer objectives in the PIS. This mapping enables a modular incorporation of decision maker’s preferences to convert any evolutionary algorithm to an interactive one, where preference information is directing the solution process. Advantages of optimizing in this new space are discussed and the idea is demonstrated with two interactive evolutionary algorithms: IOPIS/RVEA and IOPIS/NSGA-III. According to the experiments conducted, the new algorithms provide solutions that are better in quality as compared to those of state-of-the-art evolutionary algorithms and their variants where preference information is incorporated in the original objective space. Furthermore, the promising results require fewer function evaluations.
Keywords: optimisation; multi-objective optimisation; evolutionary computation; algorithms; decision support systems
Contributing organizations
Related projects
- Data-driven Decision Support with Multiobjective Optimization (DAEMON)
- Miettinen, Kaisa
- Research Council of Finland
- 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