Abstract
Flexible job-shop scheduling problem (FJSP) has aroused much attention from academia. It is known that evolutionary multitasking optimization (EMTO) is famous for solving multiple tasks simultaneously by leveraging the knowledge among tasks. To explore the universality of EMTO, an assisted-task based evolutionary multi-task genetic algorithm (MTGAA) is firstly proposed to deal with FJSP. In MTGAA, each FJSP task is equipped with a constitutive assisted task that generates a high-quality initial population according priory rules, so that the target-task is improved by using the knowledge from assisted-task. For the purpose to improve the ability of searching optimal of MTGAA, an adaptive crossover strategy is designed by using two popular crossover operators at the same time in this paper. Besides, the effectiveness of proposed two components are verified by comparing MTGAA to four variants of MTGAA. The expert mental results of MTGAA are compared with two latest algorithms on standard benchmark data instances and the experimental results show that MTGAA is competitive in dealing with FJSP.
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This work was supported by the Guangdong Basic and Applied Basic Research Foundation (2021A151511073, 2022A1515011297).
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Ning, X., Zhao, H., Liu, X., Liu, J. (2023). An Evolutionary Multi-task Genetic Algorithm with Assisted-Task for Flexible Job Shop Scheduling. In: Sun, Y., et al. Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2022. Communications in Computer and Information Science, vol 1682. Springer, Singapore. https://doi.org/10.1007/978-981-99-2385-4_27
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