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 editorsMazumdar, Atanu; Chugh, Tinkle; Hakanen, Jussi; Miettinen, Kaisa

Journal or seriesIEEE Transactions on Evolutionary Computation

ISSN1089-778X

eISSN1941-0026

Publication year2022

Publication date25/02/2022

Volume26

Issue number5

Pages range1182-1191

PublisherIEEE

Publication countryUnited States

Publication languageEnglish

DOIhttps://doi.org/10.1109/TEVC.2022.3154231

Publication open accessNot 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.


Keywordsmulti-objective optimisationGaussian processeskriging methodPareto efficiencyevolutionary computation

Free keywordsKriging; Gaussian processes; metamodelling; surrogate; kernel density estimation; Pareto optimality


Contributing organizations


Related projects


Ministry reportingYes

Reporting Year2022

JUFO rating3


Last updated on 2024-26-03 at 09:21