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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Bibliographic Details
Published inApplied soft computing Vol. 10; no. 3; pp. 888 - 896
Main Authors Xing, Li-Ning, Chen, Ying-Wu, Wang, Peng, Zhao, Qing-Song, Xiong, Jian
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.06.2010
Subjects
Ant Colony Optimization
Combinatorial optimization
Flexible Job Shop Scheduling
Ant Colony Optimization
Combinatorial optimization
Flexible Job Shop Scheduling
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Summary: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.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2009.10.006