Enhancing Whale Optimization Algorithm with Chaotic Theory for Permutation Flow Shop Scheduling Problem
The permutation flow shop scheduling problem (PFSSP) is a typical production scheduling problem and it has been proved to be a nondeterministic polynomial (NP-hard) problem when its scale is larger than 3. The whale optimization algorithm (WOA) is a new swarm intelligence algorithm which performs we...
Main Authors: | , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Springer
2021-01-01
|
Series: | International Journal of Computational Intelligence Systems |
Subjects: | |
Online Access: | https://www.atlantis-press.com/article/125951267/view |
_version_ | 1818006186649190400 |
---|---|
author | Jiang Li Lihong Guo Yan Li Chang Liu Lijuan Wang Hui Hu |
author_facet | Jiang Li Lihong Guo Yan Li Chang Liu Lijuan Wang Hui Hu |
author_sort | Jiang Li |
collection | DOAJ |
description | The permutation flow shop scheduling problem (PFSSP) is a typical production scheduling problem and it has been proved to be a nondeterministic polynomial (NP-hard) problem when its scale is larger than 3. The whale optimization algorithm (WOA) is a new swarm intelligence algorithm which performs well for PFSSP. But the stability is still low, and the optimization results are not too good. On this basis, we optimize the parameters of WOA through chaos theory, and put forward a chaotic whale algorithm (CWA). Firstly, in this paper, the proposed CWA is combined with Nawaz–Ensco–Ham (NEH) and largest-rank-value (LRV) rule to initialize the population. Next, chaos theory is applied to WOA algorithm to improve its convergence speed and stability. On this basis, we also use cross operator and reversal-insertion operator to enhance the search ability of the algorithm. Finally, the improved local search algorithm is used to optimize the job sequence to find the minimum makespan. In several experiments, different benchmarks are used to investigate the performance of CWA. The experimental results show that CWA has better performance than other scheduling algorithms. |
first_indexed | 2024-04-14T04:57:54Z |
format | Article |
id | doaj.art-6580020879a74dab8c2eee36597d9e88 |
institution | Directory Open Access Journal |
issn | 1875-6883 |
language | English |
last_indexed | 2024-04-14T04:57:54Z |
publishDate | 2021-01-01 |
publisher | Springer |
record_format | Article |
series | International Journal of Computational Intelligence Systems |
spelling | doaj.art-6580020879a74dab8c2eee36597d9e882022-12-22T02:11:06ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832021-01-0114110.2991/ijcis.d.210112.002Enhancing Whale Optimization Algorithm with Chaotic Theory for Permutation Flow Shop Scheduling ProblemJiang LiLihong GuoYan LiChang LiuLijuan WangHui HuThe permutation flow shop scheduling problem (PFSSP) is a typical production scheduling problem and it has been proved to be a nondeterministic polynomial (NP-hard) problem when its scale is larger than 3. The whale optimization algorithm (WOA) is a new swarm intelligence algorithm which performs well for PFSSP. But the stability is still low, and the optimization results are not too good. On this basis, we optimize the parameters of WOA through chaos theory, and put forward a chaotic whale algorithm (CWA). Firstly, in this paper, the proposed CWA is combined with Nawaz–Ensco–Ham (NEH) and largest-rank-value (LRV) rule to initialize the population. Next, chaos theory is applied to WOA algorithm to improve its convergence speed and stability. On this basis, we also use cross operator and reversal-insertion operator to enhance the search ability of the algorithm. Finally, the improved local search algorithm is used to optimize the job sequence to find the minimum makespan. In several experiments, different benchmarks are used to investigate the performance of CWA. The experimental results show that CWA has better performance than other scheduling algorithms.https://www.atlantis-press.com/article/125951267/viewWhale optimization algorithmChaotic mapsFlow shop schedulingMakespanLocal searchCross selection |
spellingShingle | Jiang Li Lihong Guo Yan Li Chang Liu Lijuan Wang Hui Hu Enhancing Whale Optimization Algorithm with Chaotic Theory for Permutation Flow Shop Scheduling Problem International Journal of Computational Intelligence Systems Whale optimization algorithm Chaotic maps Flow shop scheduling Makespan Local search Cross selection |
title | Enhancing Whale Optimization Algorithm with Chaotic Theory for Permutation Flow Shop Scheduling Problem |
title_full | Enhancing Whale Optimization Algorithm with Chaotic Theory for Permutation Flow Shop Scheduling Problem |
title_fullStr | Enhancing Whale Optimization Algorithm with Chaotic Theory for Permutation Flow Shop Scheduling Problem |
title_full_unstemmed | Enhancing Whale Optimization Algorithm with Chaotic Theory for Permutation Flow Shop Scheduling Problem |
title_short | Enhancing Whale Optimization Algorithm with Chaotic Theory for Permutation Flow Shop Scheduling Problem |
title_sort | enhancing whale optimization algorithm with chaotic theory for permutation flow shop scheduling problem |
topic | Whale optimization algorithm Chaotic maps Flow shop scheduling Makespan Local search Cross selection |
url | https://www.atlantis-press.com/article/125951267/view |
work_keys_str_mv | AT jiangli enhancingwhaleoptimizationalgorithmwithchaotictheoryforpermutationflowshopschedulingproblem AT lihongguo enhancingwhaleoptimizationalgorithmwithchaotictheoryforpermutationflowshopschedulingproblem AT yanli enhancingwhaleoptimizationalgorithmwithchaotictheoryforpermutationflowshopschedulingproblem AT changliu enhancingwhaleoptimizationalgorithmwithchaotictheoryforpermutationflowshopschedulingproblem AT lijuanwang enhancingwhaleoptimizationalgorithmwithchaotictheoryforpermutationflowshopschedulingproblem AT huihu enhancingwhaleoptimizationalgorithmwithchaotictheoryforpermutationflowshopschedulingproblem |