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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