Learnable ant colony optimization algorithm for solving satellite ground station scheduling problems

被引：0
|
作者：
Yao, Feng [1 ]
Xing, Li-Ning [1 ]
机构：
[1] College of Information System and Management, National University of Defense Technology
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2012年 / 34卷 / 11期
关键词：
Ant colony optimization; Knowledge; Satellite ground station; Scheduling;
D O I：
10.3969/j.issn.1001-506X.2012.11.14
中图分类号：
学科分类号：
摘要：
With the increased observing requirements, more and more satellites and ground stations are joined to the earth observing system. It is urgent to effectively allocate the satellite ground station resources using some scientific techniques. Aiming to the satellite ground station scheduling problem, a learnable ant colony optimization (LACO) algorithm is proposed. Experimental results show that LACO is a viable and effective approach for the satellite ground station scheduling problem. This approach legitimately combines the ant colony optimization model with the knowledge model, which largely pursues the integrating advantages of these models. The proposed approach provides a useful reference to the improvement of existing optimization approaches.
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页码：2270 / 2274
页数：4
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