An Adaptive Multi-Objective Evolutionary Algorithm with Two-Stage Local Search for Flexible Job-Shop Scheduling
An adaptive evolutionary algorithm with two-stage local search is proposed to solve the multi-objective flexible job-shop scheduling problem (MOFJSP). Adaptivity and efficient solving ability are the two main features. An autonomous selection mechanism of crossover operator is designed, which divide...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Springer
2020-11-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://www.atlantis-press.com/article/125945916/view |
| _version_ | 1828526464218169344 |
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| author | Yingli Li Jiahai Wang Zhengwei Liu |
| author_facet | Yingli Li Jiahai Wang Zhengwei Liu |
| author_sort | Yingli Li |
| collection | DOAJ |
| description | An adaptive evolutionary algorithm with two-stage local search is proposed to solve the multi-objective flexible job-shop scheduling problem (MOFJSP). Adaptivity and efficient solving ability are the two main features. An autonomous selection mechanism of crossover operator is designed, which divides individuals into different levels and selects the appropriate one according to the both sides' levels to improve the self-adaptation. In parameter setting, the autonomous determination and adjustment mechanism is proposed, and parameters are adjusted autonomously according to the job scale and iteration number, so as to reduce the complexity of parameter setting and further improve the adaptivity. For improving solving ability, two-stage local search mechanism is designed. The first stage is performed before the evolution operation, so that each individual has more good genes to participate in the following operation. The second stage is performed after the evolution operation to further search the optimal solutions. Finally, a large number of comparative numerical tests are carried out, compared with other excellent algorithms, the proposed algorithm has fewer parameters to be set and stronger solving ability. |
| first_indexed | 2024-12-11T21:25:13Z |
| format | Article |
| id | doaj.art-be491292594e402cb5d117281516452e |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-12-11T21:25:13Z |
| publishDate | 2020-11-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-be491292594e402cb5d117281516452e2022-12-22T00:50:21ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832020-11-0114110.2991/ijcis.d.201104.001An Adaptive Multi-Objective Evolutionary Algorithm with Two-Stage Local Search for Flexible Job-Shop SchedulingYingli LiJiahai WangZhengwei LiuAn adaptive evolutionary algorithm with two-stage local search is proposed to solve the multi-objective flexible job-shop scheduling problem (MOFJSP). Adaptivity and efficient solving ability are the two main features. An autonomous selection mechanism of crossover operator is designed, which divides individuals into different levels and selects the appropriate one according to the both sides' levels to improve the self-adaptation. In parameter setting, the autonomous determination and adjustment mechanism is proposed, and parameters are adjusted autonomously according to the job scale and iteration number, so as to reduce the complexity of parameter setting and further improve the adaptivity. For improving solving ability, two-stage local search mechanism is designed. The first stage is performed before the evolution operation, so that each individual has more good genes to participate in the following operation. The second stage is performed after the evolution operation to further search the optimal solutions. Finally, a large number of comparative numerical tests are carried out, compared with other excellent algorithms, the proposed algorithm has fewer parameters to be set and stronger solving ability.https://www.atlantis-press.com/article/125945916/viewFlexible job-shop schedulingMulti-objective optimizationEvolutionary algorithmLocal search |
| spellingShingle | Yingli Li Jiahai Wang Zhengwei Liu An Adaptive Multi-Objective Evolutionary Algorithm with Two-Stage Local Search for Flexible Job-Shop Scheduling International Journal of Computational Intelligence Systems Flexible job-shop scheduling Multi-objective optimization Evolutionary algorithm Local search |
| title | An Adaptive Multi-Objective Evolutionary Algorithm with Two-Stage Local Search for Flexible Job-Shop Scheduling |
| title_full | An Adaptive Multi-Objective Evolutionary Algorithm with Two-Stage Local Search for Flexible Job-Shop Scheduling |
| title_fullStr | An Adaptive Multi-Objective Evolutionary Algorithm with Two-Stage Local Search for Flexible Job-Shop Scheduling |
| title_full_unstemmed | An Adaptive Multi-Objective Evolutionary Algorithm with Two-Stage Local Search for Flexible Job-Shop Scheduling |
| title_short | An Adaptive Multi-Objective Evolutionary Algorithm with Two-Stage Local Search for Flexible Job-Shop Scheduling |
| title_sort | adaptive multi objective evolutionary algorithm with two stage local search for flexible job shop scheduling |
| topic | Flexible job-shop scheduling Multi-objective optimization Evolutionary algorithm Local search |
| url | https://www.atlantis-press.com/article/125945916/view |
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