Skip to main content
. 2022 Sep 29;10(10):2438. doi: 10.3390/biomedicines10102438
Algorithm 3: Evolution of FAM Network

  1. Pop_best_fit = DE(FNT)

  2. Initialize β = 1.0 and CR = 0.7

  3. Set Generation Count t = 0(max = G)

  4. Create the initial population C(t) = NP

  5. While (stopping criteria not true)

  6. For each target xi((G)ϵC(G)

  7. Create the Mutant Vector Vi(G)

  8. Create the test vector Ui(G)

  9. For all i

  10. Evaluate F(xi(G)) and F(Ui(G))

  11. If (f(xi(t) ≥ f(Ui(t))

  12. xi(t + 1) = x0(t)

  13. else xi(t + 1) = U0(t)

  14. Return population with best fitness

  15. End