Academic Journal of Computing & Information Science, 2022, 5(11); doi: 10.25236/AJCIS.2022.051101.
Shanghai Maritime University, Shanghai, 200120, China
In this paper, the traditional genetic algorithm is improved, through MSOS coding, combined with IPOX and MPX adaptive crossover operators, the fitness function is improved, the adaptive crossover mutation probability function is introduced, and the simulation experiments are carried out on two standard instances of MK01 and 8×8. The solution of the 8×8 standard instance with the traditional genetic algorithm verifies the effectiveness and superiority of the improved genetic algorithm proposed in this paper.
Flexible job-shop Scheduling, Genetic Algorithm, Algorithm improving, MSOS
Yujie Shen. Application of Improved Genetic Algorithm in FJSP. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 11: 1-9. https://doi.org/10.25236/AJCIS.2022.051101.
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