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An Improved Artificial Bee Colony Algorithm With Q-Learning for Solving Permutation Flow-Shop Scheduling Problems
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DOI：10.1109/TSMC.2022.3219380.png)
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A permutation flow-shop scheduling problem (PFSP) has been studied for a long time due to its significance in real-life applications. This work proposes an improved artificial bee colony (ABC) algorithm with Q-learning, named QABC, for solving it with minimizing the maximum completion time (makespan). First, the Nawaz-Enscore-Ham (NEH) heuristic is employed to initialize the population of ABC. Second, a set of problem-specific and knowledge-based neighborhood structures are designed in the employ bee phase. Q-learning is employed to favorably choose the premium neighborhood structures. Next, an all-round search strategy is proposed to further enhance the quality of individuals in the onlooker bee phase. Moreover, an insert-based method is applied to avoid local optima. Finally, QABC is used to solve 151 well-known benchmark instances. Its performance is verified by comparing it with the state-of-the-art algorithms. Experimental and statistical results demonstrate its superiority over its peers in solving the concerned problems.
Keyword:
Artificial bee colony (ABC)
makespan
permutation flow-shop scheduling
Q-learning
期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
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Improved Meta-Heuristics for Solving Distributed Lot-Streaming Permutation Flow Shop Scheduling Problems改进的元启发式算法，用于解决分布式批量流置换流水车间调度问题
PREAI
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