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https://doi.org/10.1016/j.compchemeng.2019.106567
Title: | Surrogate-based black-box optimisation via domain exploration and smart placement | Authors: | SUSHANT SUHAS GARUD Nivethitha Mariappan KARIMI,IFTEKHAR ABUBAKAR |
Issue Date: | 2-Nov-2019 | Publisher: | Elsevier | Citation: | SUSHANT SUHAS GARUD, Nivethitha Mariappan, KARIMI,IFTEKHAR ABUBAKAR (2019-11-02). Surrogate-based black-box optimisation via domain exploration and smart placement 130. ScholarBank@NUS Repository. https://doi.org/10.1016/j.compchemeng.2019.106567 | Abstract: | In the era of digital twins, the need for accurately mimicking the reality has given rise to complex, black-box, compute-intensive models that are vital for simulating, analysing, and optimising physicochemical systems. In this work, we propose a novel surrogate-assisted approach for black-box optimisation, which uses efficient domain exploration and smart adaptive sample placement to escape local valleys (traps) and obtain a global minimum efficiently. Our iterative algorithm comprises two stages. The first stage constructs sub-regions based on Delaunay triangulations and selects the best for exploration. The second stage adds a new sample point to the best sub-region via optimisation. The two stages together balance domain exploration versus exploitation. The algorithmic framework is illustrated using the six-hump camel back function. An extensive numerical evaluation using twenty test functions (up to six variables) shows that the proposed algorithm exhibits superior performance against seven well-known commercial global optimisation algorithms including a surrogate-based approach. | URI: | https://scholarbank.nus.edu.sg/handle/10635/168915 | ISSN: | 0098-1354 | DOI: | 10.1016/j.compchemeng.2019.106567 |
Appears in Collections: | Elements Staff Publications |
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