A Feature Weighted Fuzzy Clustering Algorithm Based on Multistrategy Grey Wolf Optimization

Algorithm 3

MSGWO-WFCM.

	Input: dataset X = {x1,…,xN}, number of clusters K, parameters m and t, number of population n, initial value ainitial and terminal value afinal of the convergence factor, threshold parameter ξ, maximum iterations tmax, and fitness value ft(Vt) of t generation population
	Output: clustering result vector q
(1)	Use the strategy of population diversity to initialize n populations. The centroid matrix of the ith population is Vi (i = 1,…,n);
(2)	Initialize the weight matrix Wi corresponding to the i-th particle;
(3)	Let t = 1;
(4)	for i = 1 to n
(5)	Update uci with equation (5);
(6)	Update wcd with equation (7);
(7)	Update the fitness value f1(Vi) of the ith population with equation (15);
(8)	end for
(9)	while t < tmax
(10)	Select the best three wolves xα, xβ, and xδ according to the fitness value;
(11)	Calculate the value of convergence factor a according to equation (14), and calculate the values of A and C according to equations (9) and (10);
(12)	Update the position of each individual according to equations (11)–(13);
(13)	Implement the GOBL strategy for all individuals in current population, and update the position of each individual;
(14)	t = t+1;
(15)	for i = 1 to n
(16)	Update uci with equation (5);
(17)	Update wcd with equation (7);
(18)	Update the fitness value ft(Vi) of the ith population with equation (15);
(19)	end for
(20)	if (min ft(Vi) – min ft-1(Vi))<ξ
(21)	break;
(22)	end if
(23)	end while
(24)	Output membership matrix U
(25)