TOPSIS Method Based on Canberra Distance and Its Applications.

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Bibliographic Details
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]
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Database:	Engineering Source

Description
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]
ISSN:	1819656X