Adaptive Genetic Algorithm Based on Individual Similarity to Solve Multi-Objective Flexible Job-Shop Scheduling Problem
Aiming at the coupling of energy consumption and completion time in flexible job-shop scheduling, this paper took makespan and energy consumption as the optimization objectives, established a scheduling model, and proposed a scheduling strategy based on improved genetic algorithm. Firstly, a hybrid...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9762751/ |
| _version_ | 1818195634105090048 |
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| author | Xu Liang Yifan Liu Xiaolin Gu Ming Huang Fajun Guo |
| author_facet | Xu Liang Yifan Liu Xiaolin Gu Ming Huang Fajun Guo |
| author_sort | Xu Liang |
| collection | DOAJ |
| description | Aiming at the coupling of energy consumption and completion time in flexible job-shop scheduling, this paper took makespan and energy consumption as the optimization objectives, established a scheduling model, and proposed a scheduling strategy based on improved genetic algorithm. Firstly, a hybrid initialization method based on global minimum completion time selection and global minimum workload selection is introduced to generate the initial population, and the scale of the initial population is expanded to increase the diversity of the population; Secondly, the generation method of offspring individuals is improved, grouped according to the non-dominated ranking level and crowding degree of individuals in the population, and the self-contained individuals are generated by performing crossover and mutation, neighborhood search simulated annealing and reverse learning crossover mutation operations respectively. Finally, an improved adaptive crossover and mutation operation based on individual similarity is proposed, which is applied to the algorithm to improve the search ability of the algorithm. Relevant experimental results show that the proposed adaptive genetic algorithm based on individual similarity is feasible and effective in flexible job-shop scheduling. |
| first_indexed | 2024-12-12T01:21:18Z |
| format | Article |
| id | doaj.art-911916415dfc4fdbbb02a7668e4c48e0 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-12T01:21:18Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-911916415dfc4fdbbb02a7668e4c48e02022-12-22T00:43:13ZengIEEEIEEE Access2169-35362022-01-0110457484575810.1109/ACCESS.2022.31700329762751Adaptive Genetic Algorithm Based on Individual Similarity to Solve Multi-Objective Flexible Job-Shop Scheduling ProblemXu Liang0Yifan Liu1https://orcid.org/0000-0003-0287-3684Xiaolin Gu2https://orcid.org/0000-0002-4915-7573Ming Huang3https://orcid.org/0000-0002-7429-9442Fajun Guo4Computer School, Beijing Information Science and Technology University, Beijing, ChinaSoftware Technology Institute, Dalian Jiaotong University, Dalian, ChinaSchool of Computer and Communication Engineering, Dalian Jiaotong University, Dalian, ChinaSoftware Technology Institute, Dalian Jiaotong University, Dalian, ChinaSoftware Technology Institute, Dalian Jiaotong University, Dalian, ChinaAiming at the coupling of energy consumption and completion time in flexible job-shop scheduling, this paper took makespan and energy consumption as the optimization objectives, established a scheduling model, and proposed a scheduling strategy based on improved genetic algorithm. Firstly, a hybrid initialization method based on global minimum completion time selection and global minimum workload selection is introduced to generate the initial population, and the scale of the initial population is expanded to increase the diversity of the population; Secondly, the generation method of offspring individuals is improved, grouped according to the non-dominated ranking level and crowding degree of individuals in the population, and the self-contained individuals are generated by performing crossover and mutation, neighborhood search simulated annealing and reverse learning crossover mutation operations respectively. Finally, an improved adaptive crossover and mutation operation based on individual similarity is proposed, which is applied to the algorithm to improve the search ability of the algorithm. Relevant experimental results show that the proposed adaptive genetic algorithm based on individual similarity is feasible and effective in flexible job-shop scheduling.https://ieeexplore.ieee.org/document/9762751/Hybrid initializationadaptive genetic algorithmopposition-based learningadaptive crossover-mutation |
| spellingShingle | Xu Liang Yifan Liu Xiaolin Gu Ming Huang Fajun Guo Adaptive Genetic Algorithm Based on Individual Similarity to Solve Multi-Objective Flexible Job-Shop Scheduling Problem IEEE Access Hybrid initialization adaptive genetic algorithm opposition-based learning adaptive crossover-mutation |
| title | Adaptive Genetic Algorithm Based on Individual Similarity to Solve Multi-Objective Flexible Job-Shop Scheduling Problem |
| title_full | Adaptive Genetic Algorithm Based on Individual Similarity to Solve Multi-Objective Flexible Job-Shop Scheduling Problem |
| title_fullStr | Adaptive Genetic Algorithm Based on Individual Similarity to Solve Multi-Objective Flexible Job-Shop Scheduling Problem |
| title_full_unstemmed | Adaptive Genetic Algorithm Based on Individual Similarity to Solve Multi-Objective Flexible Job-Shop Scheduling Problem |
| title_short | Adaptive Genetic Algorithm Based on Individual Similarity to Solve Multi-Objective Flexible Job-Shop Scheduling Problem |
| title_sort | adaptive genetic algorithm based on individual similarity to solve multi objective flexible job shop scheduling problem |
| topic | Hybrid initialization adaptive genetic algorithm opposition-based learning adaptive crossover-mutation |
| url | https://ieeexplore.ieee.org/document/9762751/ |
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