Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
497192 | Applied Soft Computing | 2010 | 9 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science Applications
Authors
Li-Ning Xing, Ying-Wu Chen, Peng Wang, Qing-Song Zhao, Jian Xiong,