Hybrid Fuzzy Clustering Method Based on FCM and Enhanced Logarithmical PSO (ELPSO)
Algorithm 2
FCM-ELPSO.
Notations:
P: the population of ELPSO; ω_initial: the initial inertia weight of ELPSO; : the inertia weight of the particle l; and : acceleration coefficients; : the position of the particle l; : velocity vector of the particle l; : the best position that particle l has achieved at instant t; : the best position achieved by the swarm at instant t; : the membership degree matrix of the particle l; : the fitness value of the particle l; T_PSO: the maximum number of iterations in PSO part; T_FCM: the maximum number of iterations in FCM part; m: the level of cluster fuzziness;
Input: dataset S and number of clusters C;
Output: the best position .
Process:
(1)
Create a swarm with P particles;
(2)
Initialize parameters for ELPSO including size of population P; ω_initial for each particle (l= 1, 2, 3, …, P); and ,m;
(3)
Initialize ,, and for each particle (l= 1, 2, 3, …, P) and for the swarm;
(4)
do {
ELPSO:
Repeat {
(5)
Calculate the membership degree matrix of each particle;
(6)
Calculate the criterion of each particle;
(7)
Calculate the of each particle;
(8)
Calculate the of the swarm;
(9)
Update the velocity of each particle using equation (13);
(10)
Update the position of each particle using equation (14);
(11)
For each particle (l= 1, 2, 3, …, P) update using equation (12);
(∗) when it reaches 95 iterations (T_PSO) or there is a variation less than or equal to 0.00001 on criterion J.
(∗∗) when it reaches 5 iterations (T_FCM) or there is a variation less than or equal to 0.00001 on criterion J.
(∗∗∗) when it reaches 500 total iterations (ELPSO + FCM) or when there are no changes to the in two consecutive runs of the FCM–PSO (FPSO followed by FCM).