TOPSIS Method Based on Canberra Distance and Its Applications.
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| Title: | TOPSIS Method Based on Canberra Distance and Its Applications. |
|---|---|
| Authors: | Chang, Huilong1 1209685638@qq.com, Zou, Limin2 307376216@qq.com |
| Source: | IAENG International Journal of Computer Science. Jun2026, Vol. 53 Issue 6, p2272-2283. 12p. |
| Subjects: | TOPSIS method, Multiple criteria decision making, Decision theory, Mathematical optimization, Sensitivity analysis, Decision making |
| Abstract: | As an important branch of decision science, multiattribute decision-making plays a key role in the selection of optimal solutions in complex scenarios. The TOPSIS method, with its simplicity in computation and strong objectivity, has become one of the most commonly used methods in multiattribute decision-making. Its core principle is to rank the alternative solutions based on their proximity to the ideal solution, as measured by Euclidean distance. However, the TOPSIS method exhibits inherent limitations, such as a strong dependence of its ranking results on dimensionless processing and insufficient discrimination of differences in low-value ranges. In view of this, this paper proposes a TOPSIS method based on Canberra distance (C-TOPSIS), which not only eliminates the need for normalization but also enhances the sensitivity to differences in low-value ranges. Furthermore, to address issues such as measurement failure and insufficient discrimination caused by Canberra distance in zero-value data scenarios, an optimized model based on a translation strategy is proposed. This model enhances discrimination by maximizing the coefficient of variation of the distances between the alternative solutions and the positive ideal solution and the negative ideal solution. Numerical simulations and practical case analyses indicate that the C-TOPSIS method exhibits a high degree of consistency with both the Simple Additive Weighting method and the TOPSIS method in scenarios with both non-zero and zero-valued data. Additionally, the computational process is more straightforward. Stability analysis results show that the proposed method exhibits no significant deviation in the ranking results with minor fluctuations in the original data and the optimal translation parameters, demonstrating excellent antiinterference capability and stability. The proposed method offers a novel approach to multi-attribute decision-making problems. [ABSTRACT FROM AUTHOR] |
| Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) 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: 194196011 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: TOPSIS Method Based on Canberra Distance and Its Applications. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chang%2C+Huilong%22">Chang, Huilong</searchLink><relatesTo>1</relatesTo><i> 1209685638@qq.com</i><br /><searchLink fieldCode="AR" term="%22Zou%2C+Limin%22">Zou, Limin</searchLink><relatesTo>2</relatesTo><i> 307376216@qq.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IAENG+International+Journal+of+Computer+Science%22">IAENG International Journal of Computer Science</searchLink>. Jun2026, Vol. 53 Issue 6, p2272-2283. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22TOPSIS+method%22">TOPSIS method</searchLink><br /><searchLink fieldCode="DE" term="%22Multiple+criteria+decision+making%22">Multiple criteria decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+theory%22">Decision theory</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Sensitivity+analysis%22">Sensitivity analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: As an important branch of decision science, multiattribute decision-making plays a key role in the selection of optimal solutions in complex scenarios. The TOPSIS method, with its simplicity in computation and strong objectivity, has become one of the most commonly used methods in multiattribute decision-making. Its core principle is to rank the alternative solutions based on their proximity to the ideal solution, as measured by Euclidean distance. However, the TOPSIS method exhibits inherent limitations, such as a strong dependence of its ranking results on dimensionless processing and insufficient discrimination of differences in low-value ranges. In view of this, this paper proposes a TOPSIS method based on Canberra distance (C-TOPSIS), which not only eliminates the need for normalization but also enhances the sensitivity to differences in low-value ranges. Furthermore, to address issues such as measurement failure and insufficient discrimination caused by Canberra distance in zero-value data scenarios, an optimized model based on a translation strategy is proposed. This model enhances discrimination by maximizing the coefficient of variation of the distances between the alternative solutions and the positive ideal solution and the negative ideal solution. Numerical simulations and practical case analyses indicate that the C-TOPSIS method exhibits a high degree of consistency with both the Simple Additive Weighting method and the TOPSIS method in scenarios with both non-zero and zero-valued data. Additionally, the computational process is more straightforward. Stability analysis results show that the proposed method exhibits no significant deviation in the ranking results with minor fluctuations in the original data and the optimal translation parameters, demonstrating excellent antiinterference capability and stability. The proposed method offers a novel approach to multi-attribute decision-making problems. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) 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: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 2272 Subjects: – SubjectFull: TOPSIS method Type: general – SubjectFull: Multiple criteria decision making Type: general – SubjectFull: Decision theory Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Sensitivity analysis Type: general – SubjectFull: Decision making Type: general Titles: – TitleFull: TOPSIS Method Based on Canberra Distance and Its Applications. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chang, Huilong – PersonEntity: Name: NameFull: Zou, Limin IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1819656X Numbering: – Type: volume Value: 53 – Type: issue Value: 6 Titles: – TitleFull: IAENG International Journal of Computer Science Type: main |
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