Journal of System Simulation
Abstract
Abstract: A multi-classes teaching-learning-based optimization (MCTLBO) algorithm is proposed for the permutation flowshop scheduling problem (PFSP) by combining continuous algorithm with discrete strategy. An improved nawaz enscore ham (NEH) population initialization method based on permutation mutation is adopted, which takes into account the quality and diversity of initial solutions. In the teaching stage, discrete adaptive teaching with duplicate removal is introduced to avoid meaningless teaching processes. A new self-learning strategy based on Levy flight is added, and the self-learning in discrete stage is simulated by variable neighborhood search. Learner phase and class communication are combined to improve the efficiency of learning on the basis of ensuring the communication of excellent individuals. The standard test sets of Rec is tested, and compared with other algorithms, the validity and stability of the algorithm are verified.
Recommended Citation
Zhang, Qiwen and Zhang, Bin
(2022)
"Teaching-Learning-Based Optimization Algorithm for Permutation Flowshop Scheduling,"
Journal of System Simulation: Vol. 34:
Iss.
5, Article 12.
DOI: 10.16182/j.issn1004731x.joss.20-0983
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol34/iss5/12
First Page
1054
Revised Date
2021-01-26
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.20-0983
Last Page
1063
CLC
TP391.9
Recommended Citation
Qiwen Zhang, Bin Zhang. Teaching-Learning-Based Optimization Algorithm for Permutation Flowshop Scheduling[J]. Journal of System Simulation, 2022, 34(5): 1054-1063.
DOI
10.16182/j.issn1004731x.joss.20-0983
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