MVMOO: Mixed variable multi-objective optimisation

Manson, Jamie A.; Chamberlain, Thomas W.; Bourne, Richard A.

doi:10.1007/s10898-021-01052-9

Cited by 28 publications

(17 citation statements)

References 42 publications

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“…More recently, researchers have started to apply Bayesian optimization within chemistry optimization problems. ,− This class of optimization algorithm is a subset of Bayesian statistics, which uses probability to express certainty in future outcomes based on past observations. In the case of reaction optimization, when an experimental iteration is completed, a probabilistic model is trained to predict its reaction outcome (e.g., yield) given the reaction conditions.…”

Section: Self-optimizationmentioning

confidence: 99%

A Brief Introduction to Chemical Reaction Optimization

et al. 2023

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From the start of a synthetic chemist’s training, experiments are conducted based on recipes from textbooks and manuscripts that achieve clean reaction outcomes, allowing the scientist to develop practical skills and some chemical intuition. This procedure is often kept long into a researcher’s career, as new recipes are developed based on similar reaction protocols, and intuition-guided deviations are conducted through learning from failed experiments. However, when attempting to understand chemical systems of interest, it has been shown that model-based, algorithm-based, and miniaturized high-throughput techniques outperform human chemical intuition and achieve reaction optimization in a much more time- and material-efficient manner; this is covered in detail in this paper. As many synthetic chemists are not exposed to these techniques in undergraduate teaching, this leads to a disproportionate number of scientists that wish to optimize their reactions but are unable to use these methodologies or are simply unaware of their existence. This review highlights the basics, and the cutting-edge, of modern chemical reaction optimization as well as its relation to process scale-up and can thereby serve as a reference for inspired scientists for each of these techniques, detailing several of their respective applications.

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Section: Self-optimizationmentioning

confidence: 99%

A Brief Introduction to Chemical Reaction Optimization

et al. 2023

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“…This second analytical example is adapted from Manson et al (2021). The Pareto front for this example is concave and presents two discontinuities.…”

Section: Example 2: Analytical Problem With Discontinuous Pareto Frontmentioning

confidence: 99%

Multi-objective robust optimization using adaptive surrogate models for problems with mixed continuous-categorical parameters

Moustapha

Galimshina

Habert

et al. 2022

Struct Multidisc Optim

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Explicitly accounting for uncertainties is paramount to the safety of engineering structures. Optimization which is often carried out at the early stage of the structural design offers an ideal framework for this task. When the uncertainties are mainly affecting the objective function, robust design optimization is traditionally considered. This work further assumes the existence of multiple and competing objective functions that need to be dealt with simultaneously. The optimization problem is formulated by considering quantiles of the objective functions which allows for the combination of both optimality and robustness in a single metric. By introducing the concept of common random numbers, the resulting nested optimization problem may be solved using a general-purpose solver, herein the non-dominated sorting genetic algorithm (NSGA-II). The computational cost of such an approach is however a serious hurdle to its application in real-world problems. We therefore propose a surrogate-assisted approach using Kriging as an inexpensive approximation of the associated computational model. The proposed approach consists of sequentially carrying out NSGA-II while using an adaptively built Kriging model to estimate the quantiles. Finally, the methodology is adapted to account for mixed categorical-continuous parameters as the applications involve the selection of qualitative design parameters as well. The methodology is first applied to two analytical examples showing its efficiency. The third application relates to the selection of optimal renovation scenarios of a building considering both its life cycle cost and environmental impact. It shows that when it comes to renovation, the heating system replacement should be the priority.

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“…economic vs. environmental), which provides valuable information during the development of chemical processes. 9,10 The end-user of these systems is typically a chemist, who in general does not have an extensive knowledge of programming and algorithm design. However, the efficiency of the optimisations is dependent on the algorithm selected on a case-by-case basis.…”

Section: Introductionmentioning

confidence: 99%

“…economic vs. environmental), which provides valuable information during the development of chemical processes. 9,10…”

Section: Introductionmentioning

confidence: 99%