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- W4287067984 abstract "We consider computationally expensive blackbox optimization problems and present a method that employs surrogate models and concurrent computing at the search step of the mesh adaptive direct search (MADS) algorithm. Specifically, we solve a surrogate optimization problem using locally weighted scatterplot smoothing (LOWESS) models to find promising candidate points to be evaluated by the blackboxes. We consider several methods for selecting promising points from a large number of points. We conduct numerical experiments to assess the performance of the modified MADS algorithm with respect to available CPU resources by means of five engineering design problems." @default.
- W4287067984 created "2022-07-25" @default.
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- W4287067984 date "2021-07-26" @default.
- W4287067984 modified "2023-10-14" @default.
- W4287067984 title "Parallel Surrogate-assisted Optimization Using Mesh Adaptive Direct Search" @default.
- W4287067984 doi "https://doi.org/10.48550/arxiv.2107.12421" @default.
- W4287067984 hasPublicationYear "2021" @default.
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