LAPSE:2018.0144
Preprint
LAPSE:2018.0144
Deterministic Global Optimization with Artificial Neural Networks Embedded
October 15, 2018. Originally submitted on July 4, 2018
Artificial neural networks (ANNs) are used in various applications for data-driven black-box modeling and subsequent optimization. Herein, we present an efficient method for deterministic global optimization of ANN embedded optimization problems. The proposed method is based on relaxations of algorithms using McCormick relaxations in a reduced-space [\textit{SIOPT}, 20 (2009), pp. 573-601] including the convex and concave envelopes of the nonlinear activation function of ANNs. The optimization problem is solved using our in-house global deterministic solver MAiNGO. The performance of the proposed method is shown in four optimization examples: an illustrative function, a fermentation process, a compressor plant and a chemical process optimization. The results show that computational solution time is favorable compared to the global general-purpose optimization solver BARON.
Title in Alternate Language
Global deterministische Optimierung von Optimierungsproblemen mit k√ľnstlichen neuronalen Netzwerken
Suggested Citation
Schweidtmann AM, Mitsos A. Deterministic Global Optimization with Artificial Neural Networks Embedded. (2018). LAPSE:2018.0144
Author Affiliations
Schweidtmann AM: [ORCID] [Google Scholar]
Mitsos A: [ORCID] [Google Scholar]
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Journal Name
Journal of Optimization Theory and Applications
Year
2018
Publication Date
2018-10-12
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arXiv:1801.07114
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doi:10.1007/s10957-018-1396-0
Article published in Journal of Opt...
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LAPSE:2018.0144
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Oct 15, 2018
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[v2] (Pre_Print with link to published article)
Oct 15, 2018
[v1] (Original Submission)
Jul 4, 2018
 
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Original Submitter
ArturSchweidtmann
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Pre-Print Online at arXiv
Article published in Journal of Optimization Theory and Applications