Research Article
A Dynamic Opposite Learning Assisted Grasshopper Optimization Algorithm for the Flexible JobScheduling Problem
(1) | Randomly generate an initial population ; | (2) | for; ; do | (3) | ; | (4) | for; ; do | (5) | ; | (6) | Check the boundaries; | (7) | end for | (8) | end for | (9) | Select N number of the fittest individuals from ; | (10) | Set ; | (11) | while ≤ maximal iteration do | (12) | Evaluate all learners by the fitness function ; | (13) | ifthen | (14) | Sort individuals by the fitness value to get the best grasshopper in the first population; | (15) | else | (16) | for; ; do | (17) | Update the position of the individuals according to update mechanism (equation (12)); | (18) | Check the boundaries; | (19) | Evaluate the fitness values of the new individuals ; | (20) | ifthen | (21) | Replace with ; | (22) | end if | (23) | end for | (24) | end if | (25) | ; | (26) | ifthen | (27) | for; ; do | (28) | ; | (29) | for; ; do | (30) | | (31) | | (32) | Check boundaries; | (33) | end for | (34) | end for | (35) | Select N number of the fittest individuals from ; | (36) | ; | (37) | end if | (38) | end while |
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