Please use this identifier to cite or link to this item: 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
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