基于遗传模糊聚类的机群编队最优分配方法

熊伟; 丁全心; 陈宗基; 周锐

基于遗传模糊聚类的机群编队最优分配方法

1.
北京航空航天大学自动化科学与电气工程学院, 北京 100083
2. 中国航空工业第一集团公司洛阳电光设备研究所, 洛阳 471009

基金项目: 国家863资助项目(2006AA04Z260);国家自然科学资助项目基金(60674103);航空科学基金资助项目(2006ZC51026)

详细信息

中图分类号: V 221⁺.91
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- 被引次数: 0
出版历程
- 收稿日期: 2007-02-05
- 网络出版日期: 2008-02-29

Optimized formation assignment for large-scale air fleet using fuzzy clustering and genetic algorithm

1.
School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China
2. Luoyang Institute of Optic-Electronic Equipment, China Aviation Industry Corporation Ⅰ, Luoyang 471009, China

摘要

摘要: 针对当前机群的编队分配存在效率低、编队分配结果不可靠、智能性差等问题,提出了一种新的结合遗传算法和模糊聚类算法的机群编队最优分配方法.该混合算法通过模糊聚类算法解决了机群的编队分配不确定性问题,并且通过对传统遗传操作算子的改进,采用改进的遗传算法有效地克服了模糊聚类算法容易陷入局部极小值和对初始条件敏感的缺点,使机群的编队分配能快速收敛至全局最优解.3组不同分布类型的机群编队分配算例结果表明,该混合算法具有较好的通用性、有效性和智能性,适用于机群的编队最优分配.
- 编队分配 /
- 模糊聚类 /
- 遗传算法 /
- 优化
Abstract: Aiming at the low efficiency, fallibility of formation assignment result and lack of intelligence in optimized formation assignment for large-scale air fleet, a new hybrid genetic fuzzy clustering algorithm (GFCA) was proposed for large-scale air fleet optimized formation assignment by incorporating the fuzzy clustering algorithm into the genetic algorithm (GA). The GFCA solved the uncertainty problem of formation assignment for air fleet by fuzzy clustering algorithm, avoided the local minima and was robust to initialization by using improved GA, with new genetic arithmetic operators, so as to obtain the global optima for formation assignment quickly. The results of two examples show that the GFCA has better generalization, effectiveness and intelligence, and it is applicable to optimized formation assignment for large-scale air fleet.
- formation assignment /
- fuzzy clustering /
- genetic algorithms /
- optimization

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参考文献(1)

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