基于SATC-ALO和SOM神经网络的机群编队分组
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摘要
首先,分析机群编队分组问题,同时考虑了飞机属性分组模型和飞机油耗分组模型。然后,使用混沌优化算法和锦标赛选择策略优化后的SATC-ALO算法和SOM神经网络求解编队分组模型。最后,使用50组数据进行相似度计算方法和编队分组方法对比实验。实验结果表明,混合计算法方法优于欧式距离法,SATC-ALO算法分组精度最高,并且满足实时性要求,但需要事先指定分组数目,而SOM神经网络的分组精度稍低于SATC-ALO算法,但分组时间优于SATC-ALO算法,并且不需要指定分组数目。2种方法均可以更好地解决编队分组问题,具有实际应用价值。
Firstly, the problem of group-air grouping is analyzed to introduce the aircraft attribute grouping model and aircraft fuel consumption grouping model. Then, SATC-ALO optimized by Chaos optimization algorithm and Tournament Selection strategy and SOM neural network are used to solve the formation grouping model. Finally, comparative experiments of similarity calculation method and formation grouping method were performed with 50 groups of data. The experimental results show that hybrid method is superior to Euclidean distance method. SATC-ALO algorithm has the highest grouping accuracyand meets the real-time requirements. However, the number of groups needs to be specified in advance. The accuracy of SOM neural network grouping is slightly lower than SATC-ALO algorithm, but the grouping time is lower than SATC-ALO algorithm, and there is no need to specify the number of groups. Both SOM neural network and SATC-ALO algorithm can perfectly solve the problem of group-air grouping and have practical application value.
Firstly, the problem of group-air grouping is analyzed to introduce the aircraft attribute grouping model and aircraft fuel consumption grouping model. Then, SATC-ALO optimized by Chaos optimization algorithm and Tournament Selection strategy and SOM neural network are used to solve the formation grouping model. Finally, comparative experiments of similarity calculation method and formation grouping method were performed with 50 groups of data. The experimental results show that hybrid method is superior to Euclidean distance method. SATC-ALO algorithm has the highest grouping accuracyand meets the real-time requirements. However, the number of groups needs to be specified in advance. The accuracy of SOM neural network grouping is slightly lower than SATC-ALO algorithm, but the grouping time is lower than SATC-ALO algorithm, and there is no need to specify the number of groups. Both SOM neural network and SATC-ALO algorithm can perfectly solve the problem of group-air grouping and have practical application value.
引文
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[4] 齐玲辉,张安,曹璐.基于加权双质心支持向量聚类的集群编队分组[J].系统工程与电子技术,2014,36(11):2213-2218QI Linghui,ZHANG An,CAO Lu.Double Centroids-Weighted Support Vector Clustering Algorithm for Group-Air Grouping[J].Systems Engineering and Electronics,2014,36(11):2213-2218 (in Chinese)
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[6] SCHUBERT J.Reliable Force Aggregation Using a Refined Evidence Specification from Dempster-Shafer Clustering[C]//Proceeding of the 4th Annual Conference on Information Fusion Montreal,Canada,2012
[7] CANTWELL J,SCHUBERT J,WALTER J.Conflict-Based Force Aggregation[C]//Proceeding of the 6th International Command and Control Research and Technology Symposium.Annapolis,USA,2009
[8] 蔡益朝.态势评估中的兵力聚合技术研究[D].长沙:国防科学技术大学,2006CAI Yichao.Reseach on Foree Aggregation Technology inm Situation Assessment[D].Changsha,National University of Defense Technology,2006 (in Chinese)
[9] 李伟生,王宝树.态势估计中基于模糊集理论的目标编群方法[J].系统工程与电子技术,2005,27(7):1235-1237LI Weisheng,WANG Baoshu.Target Classification Method for Situation Assessment Based on Fuzzy Sets[J].System Engineering and Electronics,2005,27(7):1235-1237 (in Chinese)
[10] 匡芳君,金忠,徐蔚鸿,等.Tent混沌人工蜂群与粒子群混合算法[J].控制与决策,2015,30(5):839-846KUANG Fangjun,JIN Zhong,XU Weihong,et al.Hybridization Algorithm of Tecnt Chaos Artificial Bee Colony and Particle Swarm Optimization[J].Control Theory & Applications,2015,30(5):839-846 (in Chinese)
[11] BAO L,ZENG J C.Comparison and Analysis of the Selection Mechanism in the Artificial Bee Colony Algorithm[C]//The 9th International Conference on Hybrid Intelligent Systems,Los Alamitos,CA,2009:411-416
[12] GAO W F,LIU S Y.A Modified Artificial Bee Colony Algorithm[J].Computer & Operations Research,2012,39(3):687-697
[13] KOHONEN T.Self-Organizing Maps[J].Springer Series in Information Science,1995,13(2):47-55
[14] MIRJALILI S.The Ant Lion Optimizer[J].Advances in Engineering Software,2015,83(9):80-98
[15] BAY Sd,SCHWABACHER M.Mining Distance-Based Outliers in Near Linear Time with Randomization and a Simple Pruning Rule[C]//Proceedings of the 9th ACM Sigkdd International Conference on Knowledge Discovery and Data Mining,New York,2003