Research Article
Optimization of Heterogeneous Container Loading Problem with Adaptive Genetic Algorithm
Table 2
Adaptive genetic algorithm.
| | Step | AGA (adaptive genetic algorithm) |
| | (1) | Input: | | (2) | Generate a group of initial population encoding | | (3) | for to do | | (4) | decode with heuristic rules | | (5) | calculate the fitness value of | | (6) | search the individual with the highest fitness value, whose fitness is | | (7) | do the selection operator to , the result is | | (8) | do partial mapped crossover operator to the former part coding of | | (9) | do two point crossover operator to the latter part coding of | | (10) | store the result as | | (11) | do sequence reversed mutation operator to the former part of | | (12) | do basic bit mutation operator to the latter part of | | (13) | store the mutation result as | | (14) | decode with heuristic rules | | (15) | calculate the fitness value of | | (16) | search the individual with the highest fitness | | value in , whose fitness is | | (17) | search the individual with the lowest fitness | | value in , whose fitness value is | | (18) | if then | | (19) | replace in with in | | (20) | end if | | (21) | | | (22) | end for | | (23) | search the individual with highest fitness value in the generation, whose fitness is | | (24) | Output: , |
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