An interactive surrogate-based method for computationally expensive multiobjective optimisation
Tabatabaei, M., Hartikainen, M., Sindhya, K., Hakanen, J., & Miettinen, K. (2019). An interactive surrogate-based method for computationally expensive multiobjective optimisation. Journal of the Operational Research Society, 70(6), 898-914. https://doi.org/10.1080/01605682.2018.1468860
Published inJournal of the Operational Research Society
© 2018 Operational Research Society
Many disciplines involve computationally expensive multiobjective optimisation problems. Surrogate-based methods are commonly used in the literature to alleviate the computational cost. In this paper, we develop an interactive surrogate-based method called SURROGATE-ASF to solve computationally expensive multiobjective optimisation problems. This method employs preference information of a decision-maker. Numerical results demonstrate that SURROGATE-ASF efficiently provides preferred solutions for a decision-maker. It can handle different types of problems involving for example multimodal objective functions and nonconvex and/or disconnected Pareto frontiers.
PublisherPalgrave Macmillan Ltd.
Publication in research information system
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Related funder(s)Academy of Finland
Funding program(s)Academy Project, AoF
Additional information about fundingThis work was partly funded by the COMAS Doctoral Program at the University of Jyvaskyla, the Academy of Finland [project No. 287496], Early Career Scheme (ECS) sponsored by the Research Grants Council of Hong Kong [project No. 21201414 (Dr. Matthias Hwai Yong Tan)] and the KAUTE Foundation.
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