基于启发式自适应离散差分进化算法的多UCAV协同干扰空战决策

薛羽; 庄毅; 张友益; 倪思如; 赵学健

doi:10.7527/S1000-6893.2013.0039

航空学报 >

2013 , Vol. 34 >Issue 2: 343 - 351

DOI: https://doi.org/10.7527/S1000-6893.2013.0039

电子与控制

基于启发式自适应离散差分进化算法的多UCAV协同干扰空战决策

薛羽 ,
庄毅 ,
张友益 ,
倪思如 ,
赵学健

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1. 南京航空航天大学计算机科学与技术学院, 江苏南京 210016;
2. 中船重工集团公司第723研究所, 江苏扬州 225001;
3. 南京邮电大学物联网学院, 江苏南京 210046

薛羽,男,博士研究生。主要研究方向:智能计算、电子对抗、物联网。Tel:025-84890790,E-mail:xueyu_123@nuaa.edu.cn;庄毅,女,教授,博士生导师。主要研究方向:分布计算。Tel:025-84896779,E-mail:zhuangyi@nuaa.edu.cn

收稿日期: 2012-02-22

修回日期: 2012-04-16

网络出版日期: 2012-05-17

基金资助

江苏省普通高校研究生科研创新计划(CXLX11_0203);航空科学基金(2010ZC13012);国防基础研究基金(Q072006C002-1)

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Multiple UCAV Cooperative Jamming Air Combat Decision Making Based on Heuristic Self-adaptive Discrete Differential Evolution Algorithm

XUE Yu ,
ZHUANG Yi ,
ZHANG Youyi ,
NI Siru ,
ZHAO Xuejian

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1. School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;
2. No.723 Institute, China Shipbuilding Industry Corporation, Yangzhou 225001, China;
3. College of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210046, China

Received date: 2012-02-22

Revised date: 2012-04-16

Online published: 2012-05-17

Supported by

Funding of Jiangsu Innovation Program for Graduate Education (CXLX11_0203); Aeronautical Science Foundation of China (2010ZC13012); National Defense Foundation of China (Q072006C002-1)

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摘要

研究了多无人作战飞机(UCAV)协同干扰空战决策(MUCJAD)问题,在干扰效能评估指标量化方法的基础上为该问题建立了优化模型。为有效求解该模型,提出一种启发式自适应离散差分进化(H-SDDE)算法。在H-SDDE算法中,设计了包含4种候选解产生策略的候选策略池,引入了候选解产生策略及其参数的自适应学习过程。此外,结合实际问题为算法设计了基于威胁度的扩展型整数编码方案、基于威胁度的启发式个体调整操作、基于约束满足的个体修复操作。在12个测试实例上进行了仿真验证,结果表明,H-SDDE算法与其他同类算法相比在求解质量和求解速度上具有明显优势,能够更好地发挥多UCAV协同干扰整体效果。

关键词： 无人机; 协同干扰; 决策; 启发式算法; 自适应; 优化; 差分进化

本文引用格式

薛羽 , 庄毅 , 张友益 , 倪思如 , 赵学健 . 基于启发式自适应离散差分进化算法的多UCAV协同干扰空战决策[J]. 航空学报, 2013 , 34(2) : 343 -351 . DOI: 10.7527/S1000-6893.2013.0039

Abstract

This paper presents a multiple unmanned combat air vehicle (UCAV) cooperative jamming air combat decision-making (MUCJAD) problem. An optimal model is constructed for the MUCJAD on the metric methods of the jamming effect evaluating indexes. In order to solve the model effectively, a heuristic self-adaptive discrete differential evolution (H-SDDE) algorithm is proposed in which, a strategy candidate pool which including four candidate solution generating strategies is designed and two self-adaptive learning processes are introduced for candidate solution generating strategy and its parameters. Furthermore, a threat degree based extensional integer coding scheme, a heuristic individual adjust operator and a constraints satisfaction based individual repair process are designed according to the real-word application. Simulation experiments are conducted on twelve test instances. The results indicate that the H-SDDE algorithm is better than other algorithms in terms of convergence speed and solution quality. The H-SDDE algorithm can enhance the effect of multiple UCAV cooperative jamming.

Key words： unmanned aerial vehicles; cooperative jamming; decision making; heuristic algorithm; self-adaption; optimization; differential evolution

参考文献

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