An Improved Artificial Bee Colony Algorithm With Q -Learning for Solving Permutation Flow-Shop Scheduling Problems

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 (makesp...

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Bibliographic Details
Published in:IEEE transactions on systems, man, and cybernetics. Systems man, and cybernetics. Systems, 2023-05, Vol.53 (5), p.1-10
Main Authors: Li, Hanxiao, Gao, Kaizhou, Duan, Pei-Yong, Li, Jun-Qing, Zhang, Le
Format: Article
Language:English
Subjects:
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Artificial bee colony (ABC)
Artificial bee colony algorithm
Completion time
Energy consumption
Job shop scheduling
Machine learning
makespan
Mathematical models
Metaheuristics
permutation flow-shop scheduling
Permutations
Q-learning
Search algorithms
Search problems
Swarm intelligence
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Summary: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.
ISSN:2168-2216
2168-2232
DOI:10.1109/TSMC.2022.3219380