Standard

Differential evolution algorithm of solving an inverse problem for the spatial Solow mathematical model. / Kabanikhin, Sergey; Krivorotko, Olga; Bektemessov, Zholaman et al.

In: Journal of Inverse and Ill-Posed Problems, Vol. 28, No. 5, 01.10.2020, p. 761-774.

Research output: Contribution to journal › Article › peer-review

Harvard

Kabanikhin, S, Krivorotko, O, Bektemessov, Z, Bektemessov, M & Zhang, S 2020, 'Differential evolution algorithm of solving an inverse problem for the spatial Solow mathematical model', Journal of Inverse and Ill-Posed Problems, vol. 28, no. 5, pp. 761-774. https://doi.org/10.1515/jiip-2020-0108

APA

Kabanikhin, S., Krivorotko, O., Bektemessov, Z., Bektemessov, M., & Zhang, S. (2020). Differential evolution algorithm of solving an inverse problem for the spatial Solow mathematical model. Journal of Inverse and Ill-Posed Problems, 28(5), 761-774. https://doi.org/10.1515/jiip-2020-0108

Vancouver

Kabanikhin S, Krivorotko O, Bektemessov Z, Bektemessov M, Zhang S. Differential evolution algorithm of solving an inverse problem for the spatial Solow mathematical model. Journal of Inverse and Ill-Posed Problems. 2020 Oct 1;28(5):761-774. Epub 2020 Sept 1. doi: 10.1515/jiip-2020-0108

Author

Kabanikhin, Sergey ; Krivorotko, Olga ; Bektemessov, Zholaman et al. / Differential evolution algorithm of solving an inverse problem for the spatial Solow mathematical model. In: Journal of Inverse and Ill-Posed Problems. 2020 ; Vol. 28, No. 5. pp. 761-774.

BibTeX

@article{bbb3adac90a14b20af1ac641175b21de,
title = "Differential evolution algorithm of solving an inverse problem for the spatial Solow mathematical model",
abstract = "The differential evolution algorithm is applied to solve the optimization problem to reconstruct the production function (inverse problem) for the spatial Solow mathematical model using additional measurements of the gross domestic product for the fixed points. Since the inverse problem is ill-posed the regularized differential evolution is applied. For getting the optimized solution of the inverse problem the differential evolution algorithm is paralleled to 32 kernels. Numerical results for different technological levels and errors in measured data are presented and discussed. ",
keywords = "differential evolution, economy, identifiability, inverse problem, optimization, parameter identification, PDE, reconstruction of parameters, regularization, Solow model, spatial Solow model",
author = "Sergey Kabanikhin and Olga Krivorotko and Zholaman Bektemessov and Maktagali Bektemessov and Shuhua Zhang",
year = "2020",
month = oct,
day = "1",
doi = "10.1515/jiip-2020-0108",
language = "English",
volume = "28",
pages = "761--774",
journal = "Journal of Inverse and Ill-Posed Problems",
issn = "0928-0219",
publisher = "Walter de Gruyter GmbH",
number = "5",

}

RIS

TY - JOUR

T1 - Differential evolution algorithm of solving an inverse problem for the spatial Solow mathematical model

AU - Kabanikhin, Sergey

AU - Krivorotko, Olga

AU - Bektemessov, Zholaman

AU - Bektemessov, Maktagali

AU - Zhang, Shuhua

PY - 2020/10/1

Y1 - 2020/10/1

N2 - The differential evolution algorithm is applied to solve the optimization problem to reconstruct the production function (inverse problem) for the spatial Solow mathematical model using additional measurements of the gross domestic product for the fixed points. Since the inverse problem is ill-posed the regularized differential evolution is applied. For getting the optimized solution of the inverse problem the differential evolution algorithm is paralleled to 32 kernels. Numerical results for different technological levels and errors in measured data are presented and discussed.

AB - The differential evolution algorithm is applied to solve the optimization problem to reconstruct the production function (inverse problem) for the spatial Solow mathematical model using additional measurements of the gross domestic product for the fixed points. Since the inverse problem is ill-posed the regularized differential evolution is applied. For getting the optimized solution of the inverse problem the differential evolution algorithm is paralleled to 32 kernels. Numerical results for different technological levels and errors in measured data are presented and discussed.

KW - differential evolution

KW - economy

KW - identifiability

KW - inverse problem

KW - optimization

KW - parameter identification

KW - PDE

KW - reconstruction of parameters

KW - regularization

KW - Solow model

KW - spatial Solow model

UR - http://www.scopus.com/inward/record.url?scp=85092353664&partnerID=8YFLogxK

U2 - 10.1515/jiip-2020-0108

DO - 10.1515/jiip-2020-0108

M3 - Article

AN - SCOPUS:85092353664

VL - 28

SP - 761

EP - 774

JO - Journal of Inverse and Ill-Posed Problems

JF - Journal of Inverse and Ill-Posed Problems

SN - 0928-0219

IS - 5

ER -

ID: 25611380