A Constructive Data Classification Version of the Particle Swarm Optimization Algorithm

Pseudocode 1

Particle Swarm Clustering (PSC).

1. procedure  [X, V, P, g, 𝜔, PLABELS ] = PSC (Y, 𝑣_max, 𝑚, 𝜔, CLABELS)

2. Y//dataset

3. initialize X//initialize at random each particle 𝑥_𝑖 ∈ [0,1]

4. initialize 𝑣_𝑖//initialize at random each v_𝑖 ∈[−𝑣_max, 𝑣_max]

5. initialize dist

6. 𝑡=1

7. while stopping criterion is not met

8.  for 𝑗=1 to 𝑛//for each input datum

9.   for 𝑖=1 to 𝑚//for each particle

10.    dist (𝑖) = distance (𝑥_𝑖,𝑦^𝑗)

11.    end for

12.   𝐼 = index (min (dist))

13.   [ PLABELS ] = label (𝑥_𝐼, CLABELS (𝑦^𝑗))//predicted label

14.   if distance (𝑥_𝐼,𝑦^𝑗) < distance (𝑝^𝑗_𝐼,𝑦^𝑗)

15.    𝑝^𝑗_𝐼=𝑥_𝐼

16.   end if

17.   if distance (𝑥_𝐼,𝑦^𝑗) < distance (𝑔^𝑗,𝑦^𝑗)

18.    𝑔^𝑗=𝑥_𝐼

19.   end if

20.   𝑣_𝐼(𝑡+1) = 𝜔∗𝑣_𝐼(𝑡) + 𝜑₁⊗ (𝑝^𝑗_𝐼(𝑡)−𝑥_𝐼(𝑡)) + 𝜑₂⊗ (𝑔^𝑗(𝑡)−𝑥_𝐼(𝑡)) + 𝜑₃⊗ (𝑦^𝑗−𝑥_𝑖(𝑡))

21.   𝑣_𝐼 ∈ [−𝑣_max, 𝑣_max]

22.   𝑥_𝐼 (𝑡+1) = 𝑥_𝐼(𝑡) + 𝑣_𝐼 (𝑡+1)

23.   𝑥_𝐼 ∈ [0, 1]

24.  end for

25.  for 𝑖=1 to 𝑚

26.   if ( 𝑥_𝑖! = win )//particles did not 𝑤𝑖𝑛

27.    𝑣_𝑖(𝑡+1) = ω * 𝑣_𝑖 (𝑡) + φ ₄⊗ (x _{most_win} − 𝑥_𝑖(𝑡))

28.    𝑣_𝑖∈ [−𝑣_max, 𝑣_max]

29.    𝑥_𝑖(𝑡+1) = 𝑥_𝑖(𝑡) + 𝑣_𝑖(𝑡+1)

30.   end if

31.  end for

32.  𝑡=𝑡+1

33.  𝜔=0.95∗𝜔

34.  Test the stopping criterion

35. end while

36. end procedure