An interactive surrogate-based method for computationally expensive multiobjective optimisation

Mohammad Tabatabaei, Markus Hartikainen, Karthik Sindhya, Jussi Hakanen and Kaisa Miettinen

Journal of the Operational Research Society, 2019, vol. 70, issue 6, 898-914

Abstract: 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.

Date: 2019
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DOI: 10.1080/01605682.2018.1468860

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