Academic Journal
A Review of Stochastic Optimization Algorithms Applied in Food Engineering.
| Τίτλος: | A Review of Stochastic Optimization Algorithms Applied in Food Engineering. |
|---|---|
| Συγγραφείς: | Koop, Laís, Ramos, Nadia Maria do Valle, Bonilla-Petriciolet, Adrián, Corazza, Marcos Lúcio, Voll, Fernando Augusto Pedersen |
| Πηγή: | International Journal of Chemical Engineering (1687806X); 5/31/2024, Vol. 2024, p1-31, 31p |
| Θεματικοί όροι: | Optimization algorithms, Differential evolution, Swarm intelligence, Global optimization, Robust optimization, Genetic algorithms |
| Περίληψη: | Mathematical models that represent food processing operations are characterized by the nonlinearity of their dynamic behavior with possible discrete events, the existence of several variables of interest that are usually distributed in space, and the presence of nonlinear constraints. These features require robust optimization methods to resolve these models and to identify the optimum operating conditions of the processes. Stochastic optimization methods, often referred as metaheuristics, are effective and reliable tools to perform the global and multiobjective optimization of process units and operations involved in food engineering. In this way, this paper surveys recent advances and contributions that have applied stochastic methods for solving global and multiobjective optimization problems in food engineering. The description of the most used stochastic algorithms in food engineering is provided including the application of those methods classified as random search techniques, evolutionary methods, and swarm intelligence methods. It was observed that evolutionary methods are the most applied in solving food engineering optimization problems where the genetic algorithm and differential evolution stand out. Finally, remarks on the limitations and current challenges to improving the numerical performance of stochastic optimization methods for food engineering applications are also discussed. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Chemical Engineering (1687806X) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 177606094 RelevancyScore: 958 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 957.83154296875 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Review of Stochastic Optimization Algorithms Applied in Food Engineering. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Koop%2C+Laís%22">Koop, Laís</searchLink><br /><searchLink fieldCode="AR" term="%22Ramos%2C+Nadia+Maria+do+Valle%22">Ramos, Nadia Maria do Valle</searchLink><br /><searchLink fieldCode="AR" term="%22Bonilla-Petriciolet%2C+Adrián%22">Bonilla-Petriciolet, Adrián</searchLink><br /><searchLink fieldCode="AR" term="%22Corazza%2C+Marcos+Lúcio%22">Corazza, Marcos Lúcio</searchLink><br /><searchLink fieldCode="AR" term="%22Voll%2C+Fernando+Augusto+Pedersen%22">Voll, Fernando Augusto Pedersen</searchLink> – Name: TitleSource Label: Source Group: Src Data: International Journal of Chemical Engineering (1687806X); 5/31/2024, Vol. 2024, p1-31, 31p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Differential+evolution%22">Differential evolution</searchLink><br /><searchLink fieldCode="DE" term="%22Swarm+intelligence%22">Swarm intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Global+optimization%22">Global optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Robust+optimization%22">Robust optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Mathematical models that represent food processing operations are characterized by the nonlinearity of their dynamic behavior with possible discrete events, the existence of several variables of interest that are usually distributed in space, and the presence of nonlinear constraints. These features require robust optimization methods to resolve these models and to identify the optimum operating conditions of the processes. Stochastic optimization methods, often referred as metaheuristics, are effective and reliable tools to perform the global and multiobjective optimization of process units and operations involved in food engineering. In this way, this paper surveys recent advances and contributions that have applied stochastic methods for solving global and multiobjective optimization problems in food engineering. The description of the most used stochastic algorithms in food engineering is provided including the application of those methods classified as random search techniques, evolutionary methods, and swarm intelligence methods. It was observed that evolutionary methods are the most applied in solving food engineering optimization problems where the genetic algorithm and differential evolution stand out. Finally, remarks on the limitations and current challenges to improving the numerical performance of stochastic optimization methods for food engineering applications are also discussed. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of International Journal of Chemical Engineering (1687806X) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1155/2024/3636305 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 31 StartPage: 1 Subjects: – SubjectFull: Optimization algorithms Type: general – SubjectFull: Differential evolution Type: general – SubjectFull: Swarm intelligence Type: general – SubjectFull: Global optimization Type: general – SubjectFull: Robust optimization Type: general – SubjectFull: Genetic algorithms Type: general Titles: – TitleFull: A Review of Stochastic Optimization Algorithms Applied in Food Engineering. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Koop, Laís – PersonEntity: Name: NameFull: Ramos, Nadia Maria do Valle – PersonEntity: Name: NameFull: Bonilla-Petriciolet, Adrián – PersonEntity: Name: NameFull: Corazza, Marcos Lúcio – PersonEntity: Name: NameFull: Voll, Fernando Augusto Pedersen IsPartOfRelationships: – BibEntity: Dates: – D: 31 M: 05 Text: 5/31/2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 1687806X Numbering: – Type: volume Value: 2024 Titles: – TitleFull: International Journal of Chemical Engineering (1687806X) Type: main |
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