Tuning evolutionary multiobjective optimization for closed-loop estimation of chromatographic operating conditions

Richard Allmendinger; Spyridon Gerontas; Nigel J. Titchener-Hooker; Suzanne S. Farid

doi:10.1007/978-3-319-10762-2_73

Tuning evolutionary multiobjective optimization for closed-loop estimation of chromatographic operating conditions

Richard Allmendinger, Spyridon Gerontas, Nigel J. Titchener-Hooker, Suzanne S. Farid

Management Sciences

Mortimer Market Centre

Research output: Chapter in Book/Conference proceeding › Chapter › peer-review

Abstract

Purification is an essential step in the production of biopharmaceuticals. Resources are usually limited during development to make a full assessment of operating conditions for a given purification process commonly consisting of two or more chromatographic steps. This study proposes the optimization of all operating conditions simultaneously using an evolutionary multiobjective optimization algorithm (EMOA). After formulating the closed-loop optimization problem, which is subject to constraints and resourcing issues, four state-of-the-art EMOAs — NSGAII, MOEA/D, SMS-EMOA, and ParEGO — were tuned and evaluated on test problems created from real-world data available in the literature. The simulation results revealed that the performance of an EMOA depends on the setting of the population size, and constraint and resourcing issue-handling strategies adopted. Tuning these algorithm parameters revealed that the EMOAs, in particular SMS-EMOA and ParEGO, are able to discover reliably within 100 evaluations operating conditions that lead to high levels of yield and product purity.

Original language	English
Title of host publication	Parallel Problem Solving from Nature
Subtitle of host publication	PPSN XIII. PPSN 2014
Publisher	Springer Nature
Pages	741-750
Number of pages	10
ISBN (Print)	978-3-319-10761-5
DOIs	https://doi.org/10.1007/978-3-319-10762-2_73
Publication status	Published - 2014

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher	Springer Nature
Volume	8672
ISSN (Print)	0302-9743

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10.1007/978-3-319-10762-2_73

Cite this

Allmendinger, R., Gerontas, S., Titchener-Hooker, N. J., & Farid, S. S. (2014). Tuning evolutionary multiobjective optimization for closed-loop estimation of chromatographic operating conditions. In Parallel Problem Solving from Nature: PPSN XIII. PPSN 2014 (pp. 741-750). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8672). Springer Nature. https://doi.org/10.1007/978-3-319-10762-2_73

Allmendinger, Richard ; Gerontas, Spyridon ; Titchener-Hooker, Nigel J. et al. / Tuning evolutionary multiobjective optimization for closed-loop estimation of chromatographic operating conditions. Parallel Problem Solving from Nature: PPSN XIII. PPSN 2014. Springer Nature, 2014. pp. 741-750 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Allmendinger, R, Gerontas, S, Titchener-Hooker, NJ & Farid, SS 2014, Tuning evolutionary multiobjective optimization for closed-loop estimation of chromatographic operating conditions. in Parallel Problem Solving from Nature: PPSN XIII. PPSN 2014. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8672, Springer Nature, pp. 741-750. https://doi.org/10.1007/978-3-319-10762-2_73

Tuning evolutionary multiobjective optimization for closed-loop estimation of chromatographic operating conditions. / Allmendinger, Richard; Gerontas, Spyridon; Titchener-Hooker, Nigel J. et al.
Parallel Problem Solving from Nature: PPSN XIII. PPSN 2014. Springer Nature, 2014. p. 741-750 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8672).

Research output: Chapter in Book/Conference proceeding › Chapter › peer-review

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Allmendinger R, Gerontas S, Titchener-Hooker NJ, Farid SS. Tuning evolutionary multiobjective optimization for closed-loop estimation of chromatographic operating conditions. In Parallel Problem Solving from Nature: PPSN XIII. PPSN 2014. Springer Nature. 2014. p. 741-750. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-10762-2_73

Tuning evolutionary multiobjective optimization for closed-loop estimation of chromatographic operating conditions

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