Volume 37, Number 2, April 2019
|Page(s)||407 - 416|
|Published online||05 August 2019|
Group Formation Basd on SATC-ALO and SOM Neural Network
Air Traffic Control and Narigation College Force Engineering University, Xi’an 710038, China
2 959939 PLA Troops, Cangzhou 0617136, China
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.
Key words: group-air grouping / hybrid calculating method / self-adaptive tent chaos search ant lion optimizer algorithm(SATC-ALO) / self organizing maps network(SOM)
关键字 : 机群编队分组 / 混合计算方法 / 自适应Tent混沌搜索蚁狮优化算法(SATC-ALO) / SOM神经网络
© 2019 Journal of Northwestern Polytechnical University. All rights reserved.
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