A1 Journal article (refereed)
Probabilistic Selection Approaches in Decomposition-based Evolutionary Algorithms for Offline Data-Driven Multiobjective Optimization (2022)
Mazumdar, A., Chugh, T., Hakanen, J., & Miettinen, K. (2022). Probabilistic Selection Approaches in Decomposition-based Evolutionary Algorithms for Offline Data-Driven Multiobjective Optimization. IEEE Transactions on Evolutionary Computation, 26(5), 1182-1191. https://doi.org/10.1109/TEVC.2022.3154231
JYU authors or editors
Publication details
All authors or editors: Mazumdar, Atanu; Chugh, Tinkle; Hakanen, Jussi; Miettinen, Kaisa
Journal or series: IEEE Transactions on Evolutionary Computation
ISSN: 1089-778X
eISSN: 1941-0026
Publication year: 2022
Publication date: 25/02/2022
Volume: 26
Issue number: 5
Pages range: 1182-1191
Publisher: IEEE
Publication country: United States
Publication language: English
DOI: https://doi.org/10.1109/TEVC.2022.3154231
Publication open access: Not open
Publication channel open access:
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/83457
Abstract
In offline data-driven multiobjective optimization, no new data is available during the optimization process. Approximation models, also known as surrogates, are built using the provided offline data. A multiobjective evolutionary algorithm can be utilized to find solutions by using these surrogates. The accuracy of the approximated solutions depends on the surrogates and approximations typically involve uncertainties. In this paper, we propose probabilistic selection approaches that utilize the uncertainty information of the Kriging models (as surrogates) to improve the solution process in offline data-driven multiobjective optimization. These approaches are designed for decomposition-based multiobjective evolutionary algorithms and can, thus, handle a large number of objectives. The proposed approaches were tested on distance-based visualizable test problems and the DTLZ suite. The proposed approaches produced solutions with a greater hypervolume, and a lower root mean squared error compared to generic approaches and a transfer learning approach that do not use uncertainty information.
Keywords: multi-objective optimisation; Gaussian processes; kriging method; Pareto efficiency; evolutionary computation
Free keywords: Kriging; Gaussian processes; metamodelling; surrogate; kernel density estimation; Pareto optimality
Contributing organizations
Related projects
- 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: 2022
JUFO rating: 3