A Knowledge-Based Ant Colony Optimization for Flexible Job Shop Scheduling Problems
A Knowledge-Based Ant Colony Optimization (KBACO) algorithm is proposed in this paper for the Flexible Job Shop Scheduling Problem (FJSSP). KBACO algorithm provides an effective integration between Ant Colony Optimization (ACO) model and knowledge model. In the KBACO algorithm, knowledge model learn...
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| Published in | Applied soft computing Vol. 10; no. 3; pp. 888 - 896 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.06.2010
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1568-4946 1872-9681 |
| DOI | 10.1016/j.asoc.2009.10.006 |
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| Abstract | A Knowledge-Based Ant Colony Optimization (KBACO) algorithm is proposed in this paper for the Flexible Job Shop Scheduling Problem (FJSSP). KBACO algorithm provides an effective integration between Ant Colony Optimization (ACO) model and knowledge model. In the KBACO algorithm, knowledge model learns some available knowledge from the optimization of ACO, and then applies the existing knowledge to guide the current heuristic searching. The performance of KBACO was evaluated by a large range of benchmark instances taken from literature and some generated by ourselves. Final experimental results indicate that the proposed KBACO algorithm outperforms some current approaches in the quality of schedules. |
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| AbstractList | A Knowledge-Based Ant Colony Optimization (KBACO) algorithm is proposed in this paper for the Flexible Job Shop Scheduling Problem (FJSSP). KBACO algorithm provides an effective integration between Ant Colony Optimization (ACO) model and knowledge model. In the KBACO algorithm, knowledge model learns some available knowledge from the optimization of ACO, and then applies the existing knowledge to guide the current heuristic searching. The performance of KBACO was evaluated by a large range of benchmark instances taken from literature and some generated by ourselves. Final experimental results indicate that the proposed KBACO algorithm outperforms some current approaches in the quality of schedules. |
| Author | Xing, Li-Ning Xiong, Jian Wang, Peng Zhao, Qing-Song Chen, Ying-Wu |
| Author_xml | – sequence: 1 givenname: Li-Ning surname: Xing fullname: Xing, Li-Ning email: xln_2002@nudt.edu.cn, xinglining@gmail.com – sequence: 2 givenname: Ying-Wu surname: Chen fullname: Chen, Ying-Wu email: ywchen@nudt.edu.cn – sequence: 3 givenname: Peng surname: Wang fullname: Wang, Peng email: phd2999@gmail.com – sequence: 4 givenname: Qing-Song surname: Zhao fullname: Zhao, Qing-Song email: zqszqr@163.com – sequence: 5 givenname: Jian surname: Xiong fullname: Xiong, Jian email: xiongjian1984@hotmail.com |
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| Snippet | A Knowledge-Based Ant Colony Optimization (KBACO) algorithm is proposed in this paper for the Flexible Job Shop Scheduling Problem (FJSSP). KBACO algorithm... |
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| SubjectTerms | Ant Colony Optimization Combinatorial optimization Flexible Job Shop Scheduling |
| Title | A Knowledge-Based Ant Colony Optimization for Flexible Job Shop Scheduling Problems |
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