Mathematical Problems in Engineering / 2020 / Article / Alg

Research Article

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

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