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1: Input: CS matrix φM × N, measurement vector y and sparsity level K.
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2: Output: estimated solution set x′:
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| Initialization phase: |
| 3: R ≜ {indices of the q largest magnitude entries in φTy}. |
| 4: Initialize the grey wolf population matrix Xi × K with random integers between [1, N]. |
| 5: Xα = zeros (1, K), Xβ = zeros (1, K), Xδ = zeros (1, K). |
| 6: best = Secbest = thirbest = ∞. |
| 7: x′ = ø, ε = 10−5 and t = 1. |
| Reconstruction phase: |
| 8: while (t < tmax||best > ε) |
| 9: for each row i of the matrix Xi × K
do
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| 10: C = Union(R, Row #i of Xi × j) |
| 11: I ≜ {indices of the K largest magnitude entries in φc† y}. |
| 12: L = φI. |
| 13: Calculate the fitness value f(L) using Equation 12. |
| 14. If(best > f(L)), then
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| 15: Xα = I, |
| 16: Else If (best < f(L) && Secbest > f(L)), then
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| 17: Secbest = f(L) and Xβ = I. |
| 18: Else If (best < f(L) && Secbest < f(L) && thirbest > f(L)), then
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| 19: thirbest = f(L) and Xδ = I. |
| 20: End If
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| 21: Set R = I. |
| 22: end for
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| 23: Wolf positions updating step: |
| 24: Update a, A, and C |
| 25: for each search agent |
| 26: Update the position of the current search agent by Equation (13). |
| 27: end for
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| 28: t = t + 1 |
| 29: End while
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| 30: return x′ where x′I = L†y and x′S−I = L†y where S = [1, 2… N]. |
