کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
497192 | 862878 | 2010 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A Knowledge-Based Ant Colony Optimization for Flexible Job Shop Scheduling Problems
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزارهای علوم کامپیوتر
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Soft Computing - Volume 10, Issue 3, June 2010, Pages 888–896
Journal: Applied Soft Computing - Volume 10, Issue 3, June 2010, Pages 888–896
نویسندگان
Li-Ning Xing, Ying-Wu Chen, Peng Wang, Qing-Song Zhao, Jian Xiong,