Knowledge-Based Ant Colony Optimization for Flexible Job Shop Scheduling Problems

被引：207
|
作者：
Xing, Li-Ning [1 ]
Chen, Ying-Wu [1 ]
Wang, Peng [1 ]
Zhao, Qing-Song [1 ]
Xiong, Jian [1 ]
机构：
[1] Natl Univ Def Technol, Coll Informat Syst & Management, Dept Management Sci & Engn, Changsha 410073, Hunan, Peoples R China
来源：
APPLIED SOFT COMPUTING | 2010年 / 10卷 / 03期
关键词：
Combinatorial optimization; Ant Colony Optimization; Flexible Job Shop Scheduling; HYBRID GENETIC ALGORITHM; SHIFTING BOTTLENECK; TABU SEARCH; STRATEGY; FRAMEWORK;
D O I：
10.1016/j.asoc.2009.10.006
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：888 / 896
页数：9
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