Original paper
Surrogate-reformulation-assisted multitasking knowledge transfer for production optimization
Volume: 208, Pages: 109486 - 109486
Published: Jan 1, 2022
Abstract
Data-driven surrogate models, which are trained by samples to replace time-consuming numerical simulations, have been widely used to solve production optimization problems in recent years. It is a challenging and meaningful subject to research advanced surrogate-model-based methods that can obtain superior optimization performance within a limited time budget. The key is to enhance the quality of each training sample, i.e., the contribution of...
Paper Details
Title
Surrogate-reformulation-assisted multitasking knowledge transfer for production optimization
Published Date
Jan 1, 2022
Volume
208
Pages
109486 - 109486
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