6533b860fe1ef96bd12c3355
RESEARCH PRODUCT
An interactive surrogate-based method for computationally expensive multiobjective optimisation
Mohammad TabatabaeiMarkus HartikainenKarthik SindhyaJussi HakanenKaisa Miettinensubject
black-box functionsMathematicsofComputing_NUMERICALANALYSISmetamodeling techniquesachievement scalarising functioninteractive methodsmatemaattinen optimointimultiple criteria decision-making (MCDM)computational costmonitavoiteoptimointidescription
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. peerReviewed
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2019-01-01 |