Algorithm 1: Improved binary grey wolf optimization algorithm.

Input: n Number of grey wolves in the pack,    Iter Number of iterations for optimization. Output: $x_{\alpha }$ Optimal grey wolf binary position,      $f\left(x_{\alpha } \right)$ Best fitness value. 1: Begin 2: Initialize the population according to Equation (13). 3: Calculate the fitness value of the group and find $x_{\alpha },x_{\beta }$ and $x_{\delta }$. 4: While (t < Iter): 5:  For each ${wolf}_i$ population: 6:  Update ${wolf}_i$ position to a binary position according to Equations (9) and (17) 7:  end for 8:  Update a according to Equations (14)–(16). 9:  Update A and C according to Equations (3) and (4). 10: Evaluate the positions of individual wolves according to Equation (11). 11: Update $x_{\alpha }$, $x_{\beta }$ and $x_{\delta }$ according to Equations (7) and (8) 12: t = t + 1 13: end while 14: return $x_{\alpha }$ 15: End