An Improved Grey Wolf Optimization Algorithm with Variable Weights

Table 1

Pseudocode of the GWO algorithm.

Description	Pseudocode
Set up optimization	Dimension of the given problems
	Limitations of the given problems
	Population size
	Controlling parameter
	Stop criterion (maximum iteration times or admissible errors)
Initialization	Positions of all of the grey wolves including α, β, and δ wolves
Searching	While not the stop criterion, calculate the new fitness function
	Update the positions
	Limit the scope of positions
	Refresh α, β, and δ
	Update the stop criterion
	End