Dynamically Dimensioned Search Grey Wolf Optimizer Based on Positional Interaction Information

Algorithm 3

Pseudocode of the proposed DGWO algorithm.

	Input: population size N, scalar neighborhood size perturbation factor , maximum number of iterations MaxIter, number of variables , and upper bounds and lower bounds
	Output: optimal individual position and best fitness value
(1)	Randomly initialize N individuals’ position r to construct a population
(2)	Calculate the fitness value of each individual, find , , and , and set
(3)	while do
(4)	Compute the probability () of perturbing the decision variables using equation (12) and the value of the nonlinear control parameter using equation (27)
(5)	Generate uniform random numbers
(6)	for i = 1 to N do
(7)	for j = 1 to m do
(8)	if then
(9)	Calculate , , and according to equations (14)–(19)
(10)	Calculate , , and using equations (23)–(25)
(11)	Calculate , , and using equations (5)–(7)
(12)	Update current individuals’ position according to equation (26)
(13)	else
(14)	Calculate , , and according to equations (9)–(11)
(15)	Calculate , , and using equations (23)–(25)
(16)	Calculate , , and using equations (5)–(7)
(17)	Update current individuals’ position according to equation (26)
(18)	end if
(19)	end for
(20)	end for
(21)	Update , , and , i = 1, 2, 3
(22)	Evaluate the fitness value of each individual
(23)	Update , , and
(24)	Set
(25)	end while