Knowledge-Based Ant Colony Optimization for Flexible Job Shop Scheduling Problems
Knowledge-Based Ant Colony Optimization for Flexible Job Shop Scheduling Problems
复制标题
基于知识的蚁群优化解决柔性作业车间调度问题
DOI：
10.1016/j.asoc.2009.10.006
复制
发表时间：
2010-06-01
影响因子：
8.7
通讯作者：
Xiong, Jian
中科院分区：
文献类型：
--
作者：
Xing, Li-Ning;Chen, Ying-Wu;Xiong, Jian
针对柔性作业车间调度问题(FJSSP)，提出了一种基于知识的蚁群算法。KBACO算法实现了蚁群优化模型和知识模型的有效结合。在KBACO算法中，知识模型从蚁群算法的优化过程中学习一些可用的知识，然后应用已有的知识来指导当前的启发式搜索。KBACO的性能是通过从文献中获得的大量基准实例和我们自己生成的一些基准实例来评估的。最后的实验结果表明，所提出的KBACO算法在调度质量上优于现有的一些方法。(C)2009爱思唯尔B.V.保留所有权利。
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. (C) 2009 Elsevier B. V. All rights reserved.