A multi-chain surrogate-assisted hybrid optimization framework for joint identification of groundwater contaminant sources and hydrogeological parameters.
Saved in:
| Title: | A multi-chain surrogate-assisted hybrid optimization framework for joint identification of groundwater contaminant sources and hydrogeological parameters. |
|---|---|
| Authors: | Wu, Mengtian1,2,3 (AUTHOR), Huang, Xuan4,5 (AUTHOR), Xu, Pengcheng6 (AUTHOR), Chen, Han3 (AUTHOR), Yang, Xu3 (AUTHOR), Xu, Jin3 (AUTHOR), Duan, Qingyun1,2,3 (AUTHOR) qyduan@hhu.edu.cn |
| Source: | Hydrology & Earth System Sciences. 2026, Vol. 30 Issue 10, p3145-3163. 19p. |
| Subject Terms: | *Optimization algorithms, *Tabu search algorithm, *Groundwater pollution, *Prediction models, *Mathematical optimization, *Radial basis functions, *Hydrogeological modeling |
| Abstract: | Rapid and accurate identification of groundwater contaminant information and hydrogeological parameters is crucial for effective groundwater remediation and risk management. Within a simulation–optimization framework, this task is inherently posed as a mixed-variable optimization problem involving discrete parameters (e.g., source locations) and continuous ones (e.g., hydraulic heads, conductivities, and release fluxes). However, several challenges arise in this context. First, conventional optimization algorithms often exhibit slow convergence and unstable performance. Second, they typically require thousands of simulations to adequately explore the complex parameter space, resulting in prohibitive computational costs. To address these issues, this study develops a surrogate-assisted hybrid algorithm that integrates the Cooperative Search Algorithm (CSA) and Tabu Search (TS) within a synergistic multi-chain optimization framework, termed SA-CSA-TS. In each iteration, individual chains first perform independent CSA-based optimization to promote broad global exploration, after which they collaboratively refine source locations through a neighbourhood search guided by a shared tabu list. In addition, surrogate models equipped with a reconstruction strategy partially replace groundwater simulations, thereby substantially reducing the computational burden. Case studies reveal that the Radial Basis Function (RBF) outperforms other mainstream surrogate models in both accuracy and stability. Furthermore, comparative experiments confirm that the proposed SA-CSA-TS framework not only achieves higher solution accuracy but also significantly reduces computational demand, demonstrating strong potential for efficient groundwater contamination diagnosis. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: enr DbLabel: Energy & Power Source An: 194401725 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: A multi-chain surrogate-assisted hybrid optimization framework for joint identification of groundwater contaminant sources and hydrogeological parameters. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wu%2C+Mengtian%22">Wu, Mengtian</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Huang%2C+Xuan%22">Huang, Xuan</searchLink><relatesTo>4,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xu%2C+Pengcheng%22">Xu, Pengcheng</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Han%22">Chen, Han</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Xu%22">Yang, Xu</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xu%2C+Jin%22">Xu, Jin</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Duan%2C+Qingyun%22">Duan, Qingyun</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<i> qyduan@hhu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Hydrology+%26+Earth+System+Sciences%22">Hydrology & Earth System Sciences</searchLink>. 2026, Vol. 30 Issue 10, p3145-3163. 19p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Tabu+search+algorithm%22">Tabu search algorithm</searchLink><br />*<searchLink fieldCode="DE" term="%22Groundwater+pollution%22">Groundwater pollution</searchLink><br />*<searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br />*<searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br />*<searchLink fieldCode="DE" term="%22Radial+basis+functions%22">Radial basis functions</searchLink><br />*<searchLink fieldCode="DE" term="%22Hydrogeological+modeling%22">Hydrogeological modeling</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Rapid and accurate identification of groundwater contaminant information and hydrogeological parameters is crucial for effective groundwater remediation and risk management. Within a simulation–optimization framework, this task is inherently posed as a mixed-variable optimization problem involving discrete parameters (e.g., source locations) and continuous ones (e.g., hydraulic heads, conductivities, and release fluxes). However, several challenges arise in this context. First, conventional optimization algorithms often exhibit slow convergence and unstable performance. Second, they typically require thousands of simulations to adequately explore the complex parameter space, resulting in prohibitive computational costs. To address these issues, this study develops a surrogate-assisted hybrid algorithm that integrates the Cooperative Search Algorithm (CSA) and Tabu Search (TS) within a synergistic multi-chain optimization framework, termed SA-CSA-TS. In each iteration, individual chains first perform independent CSA-based optimization to promote broad global exploration, after which they collaboratively refine source locations through a neighbourhood search guided by a shared tabu list. In addition, surrogate models equipped with a reconstruction strategy partially replace groundwater simulations, thereby substantially reducing the computational burden. Case studies reveal that the Radial Basis Function (RBF) outperforms other mainstream surrogate models in both accuracy and stability. Furthermore, comparative experiments confirm that the proposed SA-CSA-TS framework not only achieves higher solution accuracy but also significantly reduces computational demand, demonstrating strong potential for efficient groundwater contamination diagnosis. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194401725 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.5194/hess-30-3145-2026 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 3145 Subjects: – SubjectFull: Optimization algorithms Type: general – SubjectFull: Tabu search algorithm Type: general – SubjectFull: Groundwater pollution Type: general – SubjectFull: Prediction models Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Radial basis functions Type: general – SubjectFull: Hydrogeological modeling Type: general Titles: – TitleFull: A multi-chain surrogate-assisted hybrid optimization framework for joint identification of groundwater contaminant sources and hydrogeological parameters. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wu, Mengtian – PersonEntity: Name: NameFull: Huang, Xuan – PersonEntity: Name: NameFull: Xu, Pengcheng – PersonEntity: Name: NameFull: Chen, Han – PersonEntity: Name: NameFull: Yang, Xu – PersonEntity: Name: NameFull: Xu, Jin – PersonEntity: Name: NameFull: Duan, Qingyun IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 05 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 10275606 Numbering: – Type: volume Value: 30 – Type: issue Value: 10 Titles: – TitleFull: Hydrology & Earth System Sciences Type: main |
| ResultId | 1 |
