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You calculate the final objective weights $w_j$ by normalizing the degree of diversification. The formula is $w_j = \frac{1 - e_j}{\sum_{j=1}^n (1 - e_j)}$, which divides each criterion's degree of diversification by the sum of all degrees of diversification.

Conditions: The entropy values $e_j$ have been calculated for all criteria.; The sum of all degrees of diversification is nonzero.

You calculate the final objective weights $w_j$ by normalizing the degree of diversification. The formula is $w_j = \frac{1 - e_j}{\sum_{j=1}^n (1 - e_j)}$, which divides each criterion's degree of diversification by the sum of all degrees of diversification.

Conditions: The entropy values $e_j$ have been calculated for all criteria.; The sum of all degrees of diversification is nonzero.

You normalize the decision matrix by dividing each entry by the sum of its own column. This column-wise operation ensures that the sum of the normalized values in each criterion column equals 1.

Conditions: The matrix contains nonnegative numerical attribute values.; The sum of each column must be nonzero.; Normalization is applied independently to each criterion column.

You normalize the decision matrix by dividing each entry by the sum of its own column. This column-wise operation ensures that the sum of the normalized values in each criterion column equals 1.

Conditions: The matrix contains nonnegative numerical attribute values.; The sum of each column must be nonzero.; Normalization is applied independently to each criterion column.