Algorithm 1: [26] SABO–VMD–WMH–KNN

Input: Original signal X. K, α, search agents N, number of iterations T, Number of neighbors K, weighting coefficients W. Output: classified labels Y

1: Function VMD(X, K, α)

2: U, K//Initialize matrix U with K rows and length(x) columns

3: for i = 1:K

4: U(i, :)//Initialize mode U(i, :) with random values

5: return U//Update mode U(i, :)

6: Function SABO(U, N, T)

7: for i = 1:T

8:   for i = 1:N

9: F(i), X(i, :)//Calculate objective function F(i) for search agent X(i, :)

10:   for i = 1:N

11: Compute mean displacement based on other agents

12: return U

13: Function WMH_KNN(U, TrainingData, K, Weights)

14: for i = 1:TestData

15: W*MH//Compute WMH distance to all training samples

16: K//Voting categories

17: return Y