Euclidean Distance — L2 norm between two vectors in criterion space
DIST-EUCLIDEAN (Euclidean Distance — L2 norm between two vectors in criterion space) is a distance multi-criteria decision-making (MCDM) method introduced by Hwang, C. L., Yoon, K. in 1981. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
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Method map
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When to use it
d_E ≥ 0. d_E = 0 iff a = b. Sensitive to scale — normalise inputs before applying if criteria have different units or ranges. Most commonly used in TOPSIS as the separation measure.
Strengths & limitations
- Follows a transparent, reproducible computational procedure that can be audited step by step.
- Handles multiple criteria of differing scales and units within a single decision matrix.
- Results depend on the chosen normalisation, weights, and parameter settings.
Sources
- Hwang, C. L., Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications. Lecture Notes in Economics and Mathematical Systems, Vol. 186, Springer-Verlag DOI: 10.1007/978-3-642-48318-9 ↗
How to cite this page
ScholarGate. (2026, June 2). Euclidean Distance — L2 norm between two vectors in criterion space. ScholarGate. https://scholargate.app/en/decision-making/dist-euclidean
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