Probabilistic Selection Approaches in Decomposition-based Evolutionary Algorithms for Offline Data-Driven Multiobjective Optimization
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
Julkaistu sarjassa
IEEE Transactions on Evolutionary ComputationPäivämäärä
2022Oppiaine
TietotekniikkaLaskennallinen tiedeMultiobjective Optimization GroupPäätöksen teko monitavoitteisestiMathematical Information TechnologyComputational ScienceMultiobjective Optimization GroupDecision analytics utilizing causal models and multiobjective optimizationTekijänoikeudet
© 2022 IEEE
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.
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https://converis.jyu.fi/converis/portal/detail/Publication/117517204
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This research was partly supported by the Academy of Finland (grant number 311877) and is related to the thematic research area DEMO (Decision Analytics utilizing Causal Models and Multiobjective Optimization) of the University of Jyväskylä.Lisenssi
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