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Multi-level guided evolution algorithm for solving fuzzy flexible job shop problem
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DOI：10.1016/j.asoc.2024.111932.png)
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Abstract
En 中文
Traditional flexible job shop scheduling problems (FJSP) are mostly limited to deterministic environments. In actual production, some uncertain factors lead to changes in job processing time. When solving multiobjective fuzzy flexible job shop scheduling problems (MOFFJSP), the existing evolutionary algorithms do not fully utilize information transfer between levels during the population evolution process. Therefore, a multilevel guided evolutionary (MLGE) optimization method is proposed to solve MOFFJSP. In the proposed MLGE method, decomposition and dominance techniques are combined to balance the diversity and convergence performance of the algorithm. Meanwhile, the idea of the Jaya algorithm approaching good individuals and distancing poor individuals is introduced. Combined with improved Jaya operator operations, it is used to guide individual evolution, preserving and inheriting solutions that perform well in terms of convergence and diversity. Finally, numerous experiments are carried out to evaluate the effectiveness of MLGE. The results show that MLGE can provide promising results for MOFFJSP. Additionally, it shows how MLGE outperforms other comparison algorithms in terms of convergence and variety of solutions.
Keywords:
Fuzzy flexible job shop scheduling
Multi-objective optimization
Multi-level guided evolution
Discrete Jaya algorithm
MOEA/D
NSGA-II
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6.6
Papers:
1.4W
Citations:
4.8W
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