Tuning Evolutionary Multiobjective Optimization for Closed-Loop Estimation of Chromatographic Operating Conditions

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

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

Cited by 4 publications

(7 citation statements)

References 13 publications

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“…Due to the limited knowledge in designing reliable and accurate simulation models for chromatography, the tuning of chromatographic operating conditions is primarily done via time‐consuming and expensive physical experimentation. Figure visualizes the experimental platform employed in one study (Allmendinger et al, ). To reduce costs and experimental time, an experiment (defined here as the evaluation of a sequence of chromatography steps) is often terminated prematurely as soon as a chromatography step (within a sequence of steps) with an unacceptable purity performance is evaluated, leading to missing objective function values.…”

Section: Applications Of Surrogate‐assisted Multicriteria Optimizationmentioning

confidence: 99%

“…To reduce costs and experimental time, an experiment (defined here as the evaluation of a sequence of chromatography steps) is often terminated prematurely as soon as a chromatography step (within a sequence of steps) with an unacceptable purity performance is evaluated, leading to missing objective function values. In another study (Allmendinger et al, ), this optimization scenario was simulated and a surrogate‐assisted optimization algorithm was proposed to cope with missing objective function values.…”

Section: Applications Of Surrogate‐assisted Multicriteria Optimizationmentioning

confidence: 99%

“…Finally, another approach to validate a surrogate‐assisted optimization algorithm is to fit a model to existing or published experimental data (i.e., data from real physical experiments) and then use this model as the fitness landscape to evaluate the objectives of a solution. This approach was used, for example, by Knowles (2013b); Allmendinger et al (). In particular, in (Allmendinger et al, ), a Kriging model was fit to two performance metrics, yield and purity, obtained experimentally from biochemical experiments, which in turn were reported in one study (GE Healthcare Life Sciences, ).…”

Section: Prospective Solutionsmentioning

confidence: 99%

“…This approach was used, for example, by Knowles (2013b); Allmendinger et al (). In particular, in (Allmendinger et al, ), a Kriging model was fit to two performance metrics, yield and purity, obtained experimentally from biochemical experiments, which in turn were reported in one study (GE Healthcare Life Sciences, ). Unfortunately, experimental data is often only published partly or in form of heatmaps (see, e.g., GE Healthcare Life Sciences, ), requiring tedious mapping of experimental inputs to outputs.…”

Section: Prospective Solutionsmentioning

confidence: 99%

“…In real‐world optimization, it is very common to use either physical experimentation or simulators to evaluate candidate solutions to a problem (see, e.g., Rechenberg, ; Jakumeit & Emmerich, ; Knowles, ; Small et al, ; Allmendinger, Gerontas, Titchener‐Hooker, & Farid, ). Such evaluation procedures can be costly and time‐consuming, and often there is only a limited budget of evaluations available.…”

Section: Introductionmentioning

confidence: 99%

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Surrogate-assisted multicriteria optimization: Complexities, prospective solutions, and business case

Allmendinger

Emmerich

Hakanen

et al. 2017

J. Multi-Crit. Decis. Anal.

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Complexity in solving real-world multicriteria optimization problems often stems from the fact that complex, expensive, and/or time-consuming simulation tools or physical experiments are used to evaluate solutions to a problem. In such settings, it is common to use efficient computational models, often known as surrogates or metamodels, to approximate the outcome (objective or constraint function value) of a simulation or physical experiment. The presence of multiple objective functions poses an additional layer of complexity for surrogate-assisted optimization.For example, complexities may relate to the appropriate selection of metamodels for the individual objective functions, extensive training time of surrogate models, or the optimal use of many-core computers to approximate efficiently multiple objectives simultaneously. Thinking out of the box, complexity can also be shifted from approximating the individual objective functions to approximating the entire Pareto front. This leads to further complexities, namely, how to validate statistically and apply the techniques developed to real-world problems. In this paper, we discuss emerging complexity-related topics in surrogate-assisted multicriteria optimization that may not be prevalent in nonsurrogate-assisted single-objective optimization. These complexities are motivated using several real-world problems in which the authors were involved. We then discuss several promising future research directions and prospective solutions to tackle emerging complexities in surrogate-assisted multicriteria optimization. Finally, we provide insights from an industrial point of view into how surrogate-assisted multicriteria optimization techniques can be developed and applied within a collaborative business environment to tackle real-world problems.

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Section: Applications Of Surrogate‐assisted Multicriteria Optimizationmentioning

confidence: 99%

Section: Applications Of Surrogate‐assisted Multicriteria Optimizationmentioning

confidence: 99%

Section: Prospective Solutionsmentioning

confidence: 99%

Section: Prospective Solutionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Surrogate-assisted multicriteria optimization: Complexities, prospective solutions, and business case

Allmendinger

Emmerich

Hakanen

et al. 2017

J. Multi-Crit. Decis. Anal.

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Efficient Approximation of Expected Hypervolume Improvement Using Gauss-Hermite Quadrature

Rahat

Chugh

Fieldsend

et al. 2022

Lecture Notes in Computer Science

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Many methods for performing multi-objective optimisation of computationally expensive problems have been proposed recently. Typically, a probabilistic surrogate for each objective is constructed from an initial dataset. The surrogates can then be used to produce predictive densities in the objective space for any solution. Using the predictive densities, we can compute the expected hypervolume improvement (EHVI) due to a solution. Maximising the EHVI, we can locate the most promising solution that may be expensively evaluated next. There are closed-form expressions for computing the EHVI, integrating over the multivariate predictive densities. However, they require partitioning of the objective space, which can be prohibitively expensive for more than three objectives. Furthermore, there are no closed-form expressions for a problem where the predictive densities are dependent, capturing the correlations between objectives. Monte Carlo approximation is used instead in such cases, which is not cheap. Hence, the need to develop new accurate but cheaper approximation methods remains. Here we investigate an alternative approach toward approximating the EHVI using Gauss-Hermite quadrature. We show that it can be an accurate alternative to Monte Carlo for both independent and correlated predictive densities with statistically significant rank correlations for a range of popular test problems.

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Heterogeneous Objectives: State-of-the-Art and Future Research

Allmendinger

Knowles

2023

Natural Computing Series

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Tuning Evolutionary Multiobjective Optimization for Closed-Loop Estimation of Chromatographic Operating Conditions

Cited by 4 publications

References 13 publications

Surrogate-assisted multicriteria optimization: Complexities, prospective solutions, and business case

Surrogate-assisted multicriteria optimization: Complexities, prospective solutions, and business case

Efficient Approximation of Expected Hypervolume Improvement Using Gauss-Hermite Quadrature

Heterogeneous Objectives: State-of-the-Art and Future Research

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