An improved parallel genetic algorithm for job shop scheduling problem
To address the drawbacks of poor search ability and proneness to premature convergence when genetic algorithms are used to solve the Job Shop Scheduling Problem, this paper proposes a novel improved parallel genetic algorithm. First, individuals in the population are classified based on their fitness, and different genetic operators are applied to different categories. Second, a new crossover method is designed to preserve the characteristics of high-quality individuals while enhancing population diversity. Finally, an efficient greedy mutation method is introduced to improve the ability to escape local optima. The proposed algorithm is tested on 11 standard benchmark instances and compared with other intelligent algorithms. Experimental results demonstrate that the proposed algorithm exhibits superior optimization performance.
Authors
- Mingfei He (ORCID: https://orcid.org/0000-0002-7841-6407)
- Shijiao Li (ORCID: https://orcid.org/0009-0000-2549-6397)
- Ming Zhai (ORCID: https://orcid.org/0000-0001-5355-7441)
- Yifan Hu (ORCID: https://orcid.org/0000-0002-7980-6626)
- Qianming Du (ORCID: https://orcid.org/0000-0002-1092-2899)
- Mingfu Zhu
- Ronghan Wei (ORCID: https://orcid.org/0000-0002-7571-8223)
- Yangjie Cao (ORCID: https://orcid.org/0000-0002-1170-4340)
- Juin J. Liou (ORCID: https://orcid.org/0000-0002-5815-5078)
- Lixin Zhang (ORCID: https://orcid.org/0000-0002-9557-2516)
- Wei Peng (ORCID: https://orcid.org/0000-0002-8846-5253)
Institutions
- Shihezi University (CN)
- North Minzu University (CN)
- Henan University of Technology (CN)
- Zhengzhou University (CN)
- Henan Energy & Chemical Industry Group (China) (CN)
- Zhengzhou University of Industrial Technology (CN)
Publication Details
- Published
- 2025-03-21
- DOI
- https://doi.org/10.1145/3745533.3745560
- Primary Topic
- Scheduling and Optimization Algorithms
- Type
- article
- Field-Weighted Citation Impact
- 0.00