Robust chemical product design via fuzzy optimisation approach

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“…Recently the effects of property prediction uncertainty in product design has been considered via the approach of fuzzy optimization [10] [11].…”

Section: Introductionmentioning

confidence: 99%

Working fluid selection for organic Rankine cycles – Impact of uncertainty of fluid properties

Frutiger

Andreasen

Liu

et al. 2016

Energy

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“…This gap has also existed in optimization models as well as another field of engineering (Ehrgott et al, 2014;Gabrel et al, 2014;Goerigk & Schöbel, 2015;Wang & Shan, 2007). (Ghodratnama et al, 2015) √ √ √ √ √ √ 16 (Pishvaee & Fazli Khalaf, 2016 (Gul & Zoubir, 2017) √ √ √ 33 (Goerigk & Schöbel, 2015) √ √ √ √ √ 34 (Gorissen, 2015) √ √ √ √ √ √ 35 (Liu et al, 2015) √ √ √ √ 36 (Sun et al, 2015) √ √ √ 37 √ √ √ √ 38 (Wu , 2015) √ √ √ √ √ 39 (Khan et al, 2015) √ √ √ 40 (Park, 2016) √ √ √ 41 (Goberna et al, 2015) √ √ √ √ 42 √ √ √ 43 (Wang & Pedrycz, 2015) √ √ √ √ √ 44 (Yu & Zeng, 2015) √ √ √ 45 (Asafuddoula et al, 2015) √ √ √ √ √ √ 46 (Dellino et al, 2015) √ √ √ √ √ √ √ √ √ 47 (Auzins et al, 2015) √ √ √ √ √ √ 48 (Cao et al, 2015) √ √ √ √ √ √ 49 (Ng et al, 2015) √ √ √ √ √ √ √ 50 (Allahverdi, 2015) √ √ …”

Section: Discussionmentioning

confidence: 99%

An overview on robust design hybrid metamodeling: Advanced methodology in process optimization under uncertainty

Parnianifard¹,

Azfanizam²,

Ariffin³

et al. 2018

10.5267/j.ijiec

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Nowadays, process optimization has been an interest in engineering design for improving the performance and reducing cost. In practice, in addition to uncertainty or noise parameters, a comprehensive optimization model must be able to attend other circumstances which might be imposed in problems under real operational conditions such as dynamic objectives, multiresponses, various probabilistic distribution, discrete and continuous data, physical constraints to design parameters, performance cost, computational complexity and multi-process environment. The main goal of this paper is to give a general overview on topics with brief systematic review and concise discussions about the recent development on comprehensive robust design optimization methods under hybrid aforesaid circumstances. Both optimization methods of mathematical programming based on Taguchi approach and robust optimization based on scenario sets are briefly described. Metamodels hybrid robust design is discussed as an appropriate methodology to decrease computational complexity in problems under uncertainty. In this context, the authors' policy is to choose important topics for giving a systematic picture to those who wish to be more familiar with recent studies about robust design optimization hybrid metamodels, also by attending real circumstances in practice. In particular, production and project management are considered as two important methodologies that could be improved by applications of advanced robust design combining with metamodel methods.

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“…Thus, genetic algorithms (GAs) are applied in the AutoDock software and AutoDock Vina 34 . Also, in 35 , fuzzy systems have been introduced to address the optimization of the chemical product design. Another important method for drug design called QSAR is derived from CADD to extract the description of the correlation among different structures from a set of molecules and the response to the target 36 .…”

Section: Related Workmentioning

confidence: 99%

Hybrid Harris hawks optimization with cuckoo search for drug design and discovery in chemoinformatics

Houssein

Hosney²,

Elhoseny

et al. 2020

Sci Rep

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one of the major drawbacks of cheminformatics is a large amount of information present in the datasets. In the majority of cases, this information contains redundant instances that affect the analysis of similarity measurements with respect to drug design and discovery. therefore, using classical methods such as the protein bank database and quantum mechanical calculations are insufficient owing to the dimensionality of search spaces. In this paper, we introduce a hybrid metaheuristic algorithm called cHHo-cS, which combines Harris hawks optimizer (HHo) with two operators: cuckoo search (cS) and chaotic maps. the role of cS is to control the main position vectors of the HHo algorithm to maintain the balance between exploitation and exploration phases, while the chaotic maps are used to update the control energy parameters to avoid falling into local optimum and premature convergence. feature selection (fS) is a tool that permits to reduce the dimensionality of the dataset by removing redundant and non desired information, then fS is very helpful in cheminformatics. FS methods employ a classifier that permits to identify the best subset of features. the support vector machines (SVMs) are then used by the proposed cHHo-cS as an objective function for the classification process in FS. The CHHO-CS-SVM is tested in the selection of appropriate chemical descriptors and compound activities. Various datasets are used to validate the efficiency of the proposed CHHO-CS-SVM approach including ten from the UCI machine learning repository. Additionally, two chemical datasets (i.e., quantitative structure-activity relation biodegradation and monoamine oxidase) were utilized for selecting the most significant chemical descriptors and chemical compounds activities. the extensive experimental and statistical analyses exhibit that the suggested CHHO-CS method accomplished much-preferred trade-off solutions over the competitor algorithms including the HHO, CS, particle swarm optimization, moth-flame optimization, grey wolf optimizer, Salp swarm algorithm, and sine-cosine algorithm surfaced in the literature. the experimental results proved that the complexity associated with cheminformatics can be handled using chaotic maps and hybridizing the meta-heuristic methods. The prediction and analysis of molecules are essential tasks in cheminformatics, which use methods from mathematics and computer science to enhance their performance. The implementation of these methods depends on databases. The processes that generate most of the affectations are the storage and retrieval of molecular structures and properties (e.g., pharmacogenomics data). Typically, the behavior of the compounds can be investigated using molecular analysis. The molecular analysis helps to develop and test molecules for decreasing the effects of specific diseases 1. One drawback associated with cheminformatics is the exponential increment of the search space owing to features in the dataset 2. However, cheminformatics is still being widely used in drug design, where ...

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Robust chemical product design via fuzzy optimisation approach

Cited by 20 publications

References 93 publications

Working fluid selection for organic Rankine cycles – Impact of uncertainty of fluid properties

Working fluid selection for organic Rankine cycles – Impact of uncertainty of fluid properties

An overview on robust design hybrid metamodeling: Advanced methodology in process optimization under uncertainty

Hybrid Harris hawks optimization with cuckoo search for drug design and discovery in chemoinformatics

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