A novel metaheuristic algorithm with applications in parameter estimation and engineering problems.
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| Title: | A novel metaheuristic algorithm with applications in parameter estimation and engineering problems. |
|---|---|
| Authors: | Liu, Huiran1 (AUTHOR), Wang, Zheng2 (AUTHOR), Fang, Zhiming1 (AUTHOR) zhmfang2015@163.com |
| Source: | RAIRO: Operations Research (2804-7303). 2025, Vol. 59 Issue 4, p2051-2085. 35p. |
| Subjects: | Optimization algorithms, Random numbers, Gaussian distribution, Parameter estimation, Natural selection |
| Abstract: | This paper develops a metaheuristic, the Natural Selection Optimization Algorithm (NSOA), taking inspiration from the natural selection and the survival, growth, and reproduction of the fittest. The algorithm uses two instruments: Normal distribution and Sigmoid function. The Normal distribution generates random numbers such that the individuals in the initial population obey a Normal distribution, which can place search agents appropriately. The Sigmoid function controls the search process of the NSOA, aiding the convergence of the algorithm. To appreciate the global search ability of the NSOA, we test the NSOA against 52 benchmark functions, a hydrogeologic parameter estimation problem, and three engineering problems. Moreover, this paper compares the NSOA with six other algorithms under the same experimental configuration. Our results show that, for most functions, the NSOA converges and extracts the optimal value faster than the other established algorithms, pointing to the novelty and practical efficacy of the NSOA. [ABSTRACT FROM AUTHOR] |
| Copyright of RAIRO: Operations Research (2804-7303) is the property of EDP Sciences 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.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 187790523 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A novel metaheuristic algorithm with applications in parameter estimation and engineering problems. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Liu%2C+Huiran%22">Liu, Huiran</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Zheng%22">Wang, Zheng</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fang%2C+Zhiming%22">Fang, Zhiming</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zhmfang2015@163.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22RAIRO%3A+Operations+Research+%282804-7303%29%22">RAIRO: Operations Research (2804-7303)</searchLink>. 2025, Vol. 59 Issue 4, p2051-2085. 35p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Random+numbers%22">Random numbers</searchLink><br /><searchLink fieldCode="DE" term="%22Gaussian+distribution%22">Gaussian distribution</searchLink><br /><searchLink fieldCode="DE" term="%22Parameter+estimation%22">Parameter estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+selection%22">Natural selection</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper develops a metaheuristic, the Natural Selection Optimization Algorithm (NSOA), taking inspiration from the natural selection and the survival, growth, and reproduction of the fittest. The algorithm uses two instruments: Normal distribution and Sigmoid function. The Normal distribution generates random numbers such that the individuals in the initial population obey a Normal distribution, which can place search agents appropriately. The Sigmoid function controls the search process of the NSOA, aiding the convergence of the algorithm. To appreciate the global search ability of the NSOA, we test the NSOA against 52 benchmark functions, a hydrogeologic parameter estimation problem, and three engineering problems. Moreover, this paper compares the NSOA with six other algorithms under the same experimental configuration. Our results show that, for most functions, the NSOA converges and extracts the optimal value faster than the other established algorithms, pointing to the novelty and practical efficacy of the NSOA. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of RAIRO: Operations Research (2804-7303) is the property of EDP Sciences 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: 10.1051/ro/2025017 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 35 StartPage: 2051 Subjects: – SubjectFull: Optimization algorithms Type: general – SubjectFull: Random numbers Type: general – SubjectFull: Gaussian distribution Type: general – SubjectFull: Parameter estimation Type: general – SubjectFull: Natural selection Type: general Titles: – TitleFull: A novel metaheuristic algorithm with applications in parameter estimation and engineering problems. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Liu, Huiran – PersonEntity: Name: NameFull: Wang, Zheng – PersonEntity: Name: NameFull: Fang, Zhiming IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 28047303 Numbering: – Type: volume Value: 59 – Type: issue Value: 4 Titles: – TitleFull: RAIRO: Operations Research (2804-7303) Type: main |
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