Optimizing force aggregation: SATC-ALO and SOM hybrid clustering model

doi:10.23919/JSEE.2026.000064

Journal of Systems Engineering and Electronics ›› 2026, Vol. 37 ›› Issue (2): 604-615.doi: 10.23919/JSEE.2026.000064

• SYSTEMS ENGINEERING • Previous Articles

Optimizing force aggregation: SATC-ALO and SOM hybrid clustering model

Zhenxing ZHANG(), Rennong YANG(), Ying ZHANG(), Qi SONG()

School of Air Traffic Control and Navigation, Air Force Engineering University, Xi’an 710051, China

Received:2024-04-07 Accepted:2026-03-27 Online:2026-04-18 Published:2026-04-30
Contact: Ying ZHANG E-mail:2207621676@qq.com;786918169@qq.com;jialnzuo@163.com;827145476@qq.com
About author:
ZHANG Zhenxing was born in 1993. He received his Ph.D. degree in artificial intelligence from University of Groningen in the Netherlands. He is a lecture in Airforce Engineering University. His research interests are artificial intelligence and deep learning algorithm. E-mail: 2207621676@qq.com

YANG Rennong was born in 1969. He received his M.S. degree from Air Force Engineering University. He is a professor in airforce engineering University. His research interests are Artificial Intelligence and deep learning algorithm. E-mail: 786918169@qq.com

ZHANG Ying was born in 1988. She received her M.S. degree from Air Force Engineering University. She is a lecture in Airforce Engineering University. Her research interests are artificial intelligence, deep learning algorithm and intelligent air combat. E-mail: jialnzuo@163.com

SONG Qi was born in 2001. He is a Ph.D. in Airforce Engineering University. He received his bachelor degree from Air Force Engineering University. His research interests are artificial intelligence, deep learning algorithm and intelligent air combat. E-mail: 827145476@qq.com
Supported by:
This work was supported by the Youth Talent Support Program of Xi’an Association for Science and Technology (0959202513098), the National Natural Science Foundation of China (62106284) and the Natural Science Foundation of Shanxi Province (2021JQ370).

Abstract

Abstract:

To overcome the limitations of traditional force aggregation methods, this paper proposes a novel clustering model integrating the self-adaptive tent chaos search ant lion optimizer (SATC-ALO) and the self-organizing map (SOM) network. The model introduces a hybrid distance calculation method to measure inter-target distances and enhances the ant lion optimization algorithm through tent chaos sequences, adaptive tent chaos search, tournament selection, and logistic chaos sequences. Aggregation accuracy is evaluated using minimum quantization error and confidence value for the SOM neural network. The model is resolved using SATC-ALO and SOM independently, with experiments demonstrating that SOM achieves fast and accurate grouping, while SATC-ALO offers higher precision but requires longer computational runtime, making it more suitable for hybrid approaches. Both methods are validated as practical solutions for force aggregation tasks.

Key words: force aggregation, fuzzy inference, hybrid calculating method, self-adaptive tent chaos search ant lion optimizer (SATC-ALO) algorithm, self organizing maps network (SOM)

Zhenxing ZHANG, Rennong YANG, Ying ZHANG, Qi SONG. Optimizing force aggregation: SATC-ALO and SOM hybrid clustering model[J]. Journal of Systems Engineering and Electronics, 2026, 37(2): 604-615.

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Types	Course/(°)	Speed/(m∙s⁻¹)	Height/m	X/m	Y/m
1	85	980	1 450	12 100	3 000
1	85	990	1 460	12510	2350
1	85	995	1 465	12513	2160
1	85	988	1 470	12210	2200
2	100	950	5 400	15310	−13480
2	100	960	5 370	15320	−13220
2	100	962	5 310	15210	−13630
2	100	958	5 340	15690	−13640
2	65	990	9 120	16650	1020
2	65	980	9 080	16950	930
2	65	970	9 040	17550	870
2	65	975	9 140	17450	1050
3	70	402	9 080	16750	−1200
4	97	200	3 000	15320	−12770
4	97	213	3 060	15620	−12370
5	90	952	10000	13010	−420
5	90	950	10150	13450	−900
5	90	955	10080	13420	100
5	90	954	10160	13990	490

Algorithm	SOM	ALO	SATC-ALO
Accuracy/%	96.1	96.7	97.2
Average running time/s	0.82	12.17	10.63
Peak memory occupancy rate /%	1.03	1.94	1.62

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Optimizing force aggregation: SATC-ALO and SOM hybrid clustering model

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