Energy Saving in Flow-Shop Scheduling Management: An Improved Multiobjective Model Based on Grey Wolf Optimization Algorithm

Algorithm

1 : MKGWO flow framework.

	Initialize the grey wolf population
	Initialize a, A, and C
	Calculate the objective values for each search agent
	Find the nondominated solutions and initialize the archive with them
	Exclude alpha from the archive temporarily to avoid selecting the same leader
	Exclude beta from the archive temporarily to avoid selecting the same leader
	Add back alpha and beta to the archive
	t = 1
	while (t < Max number of iterations).
	for each search agent
	Update the position of the current search agent by equations (6)–(16)
	end for
	Update a, A, and C
	Invoke Kalman filter by equations (18)–(24)
	Invoke reinforcement learning operator by equations (25) and (26)
	Calculate the objective values of all search agents
	Find the nondominated solutions
	Update the archive with respect to the obtained nondomination solutions
	If the archive is full
	Run the grid mechanism to omit one of the current archive solutions
	Add the new solution to the archives
	End if
	Exclude alpha from the archive temporarily to avoid selecting the same leader
	Exclude beta from the archive temporarily to avoid selecting the same leader
	Add back alpha and beta to the archive