Hybrid Brain Storm Optimization algorithm and Late Acceptance Hill Climbing to solve the Flexible Job-Shop Scheduling Problem
The Brain Storming Optimization (BSO) algorithm is a novel swarm intelligent algorithm that simulates the brainstorming process of humans. This paper presents the BSO algorithm as a solution to the Flexible Job-Shop Scheduling Problem (FJSSP). In aim to improve the global search of the BSO algorithm...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-06-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157820304596 |
| _version_ | 1818240416758104064 |
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| author | Malek Alzaqebah Sana Jawarneh Maram Alwohaibi Mutasem K. Alsmadi Ibrahim Almarashdeh Rami Mustafa A. Mohammad |
| author_facet | Malek Alzaqebah Sana Jawarneh Maram Alwohaibi Mutasem K. Alsmadi Ibrahim Almarashdeh Rami Mustafa A. Mohammad |
| author_sort | Malek Alzaqebah |
| collection | DOAJ |
| description | The Brain Storming Optimization (BSO) algorithm is a novel swarm intelligent algorithm that simulates the brainstorming process of humans. This paper presents the BSO algorithm as a solution to the Flexible Job-Shop Scheduling Problem (FJSSP). In aim to improve the global search of the BSO algorithm, a new updating strategy is proposed to adaptively perform several selection methods and neighborhood structures. Furthermore, BSO algorithm has good ability in exploring the search space by clustering the solutions and searching in each cluster independently, thus leading to slow convergence speed, to enhance the local intensification capability and to overcome the slow convergence of the BSO algorithm, we introduce Late Acceptance Hill Climbing (LAHC) with three neighborhoods to the BSO algorithm. Extensive computational experiments were carried out on four well-known benchmarks for FJSSP, and the performance of the BSO algorithm was compared with that of the proposed algorithm. The results demonstrate that the proposed algorithm outperforms the BSO algorithm. Furthermore, the proposed approach overcomes the best-known algorithms in some datasets and it is comparable with these algorithms in other datasets. |
| first_indexed | 2024-12-12T13:13:06Z |
| format | Article |
| id | doaj.art-43a9b7d76bf243f99803781193182850 |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-12-12T13:13:06Z |
| publishDate | 2022-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-43a9b7d76bf243f998037811931828502022-12-22T00:23:28ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782022-06-0134629262937Hybrid Brain Storm Optimization algorithm and Late Acceptance Hill Climbing to solve the Flexible Job-Shop Scheduling ProblemMalek Alzaqebah0Sana Jawarneh1Maram Alwohaibi2Mutasem K. Alsmadi3Ibrahim Almarashdeh4Rami Mustafa A. Mohammad5Department of Mathematics, College of Science, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, 31441, City of Dammam, Saudi Arabia; Basic & Applied Scientific Research Center, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, 31441 Dammam, Saudi ArabiaComputer Science Department, Community College Dammam, Imam Abdulrahman Bin Faisal University, Dammam, Saudi ArabiaDepartment of Mathematics, College of Science, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, 31441, City of Dammam, Saudi Arabia; Basic & Applied Scientific Research Center, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, 31441 Dammam, Saudi ArabiaDepartment of MIS, College of Applied Studies and Community Service, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia; Corresponding author at: Department of MIS, College of Applied Studies and Community Service, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, Saudi Arabia.Department of MIS, College of Applied Studies and Community Service, Imam Abdulrahman Bin Faisal University, Dammam, Saudi ArabiaComputer Information Systems Department, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O.Box 1982, Dammam, Saudi ArabiaThe Brain Storming Optimization (BSO) algorithm is a novel swarm intelligent algorithm that simulates the brainstorming process of humans. This paper presents the BSO algorithm as a solution to the Flexible Job-Shop Scheduling Problem (FJSSP). In aim to improve the global search of the BSO algorithm, a new updating strategy is proposed to adaptively perform several selection methods and neighborhood structures. Furthermore, BSO algorithm has good ability in exploring the search space by clustering the solutions and searching in each cluster independently, thus leading to slow convergence speed, to enhance the local intensification capability and to overcome the slow convergence of the BSO algorithm, we introduce Late Acceptance Hill Climbing (LAHC) with three neighborhoods to the BSO algorithm. Extensive computational experiments were carried out on four well-known benchmarks for FJSSP, and the performance of the BSO algorithm was compared with that of the proposed algorithm. The results demonstrate that the proposed algorithm outperforms the BSO algorithm. Furthermore, the proposed approach overcomes the best-known algorithms in some datasets and it is comparable with these algorithms in other datasets.http://www.sciencedirect.com/science/article/pii/S1319157820304596Brain Storming Optimization AlgorithmFlexible Job ShopNeighborhood Search StrategyLate Acceptance Hill Climbing |
| spellingShingle | Malek Alzaqebah Sana Jawarneh Maram Alwohaibi Mutasem K. Alsmadi Ibrahim Almarashdeh Rami Mustafa A. Mohammad Hybrid Brain Storm Optimization algorithm and Late Acceptance Hill Climbing to solve the Flexible Job-Shop Scheduling Problem Journal of King Saud University: Computer and Information Sciences Brain Storming Optimization Algorithm Flexible Job Shop Neighborhood Search Strategy Late Acceptance Hill Climbing |
| title | Hybrid Brain Storm Optimization algorithm and Late Acceptance Hill Climbing to solve the Flexible Job-Shop Scheduling Problem |
| title_full | Hybrid Brain Storm Optimization algorithm and Late Acceptance Hill Climbing to solve the Flexible Job-Shop Scheduling Problem |
| title_fullStr | Hybrid Brain Storm Optimization algorithm and Late Acceptance Hill Climbing to solve the Flexible Job-Shop Scheduling Problem |
| title_full_unstemmed | Hybrid Brain Storm Optimization algorithm and Late Acceptance Hill Climbing to solve the Flexible Job-Shop Scheduling Problem |
| title_short | Hybrid Brain Storm Optimization algorithm and Late Acceptance Hill Climbing to solve the Flexible Job-Shop Scheduling Problem |
| title_sort | hybrid brain storm optimization algorithm and late acceptance hill climbing to solve the flexible job shop scheduling problem |
| topic | Brain Storming Optimization Algorithm Flexible Job Shop Neighborhood Search Strategy Late Acceptance Hill Climbing |
| url | http://www.sciencedirect.com/science/article/pii/S1319157820304596 |
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