Simulation Research on Multi-aircraft Conflict Resolution Based on Improved Chaotic Ant Colony Algorithm

Authors

Liang Tong, Air Traffic Control and Navigation School, Air Force Engineering University, Xi'an 710051, China; National Key Laboratory of Air Traffic Collision Prevention, Xi'an 710051, China; PLA 95140 Troops, Huizhou 516000, China
Jie Yang, Air Traffic Control and Navigation School, Air Force Engineering University, Xi'an 710051, China; National Key Laboratory of Air Traffic Collision Prevention, Xi'an 710051, China
Xusheng Gan, Air Traffic Control and Navigation School, Air Force Engineering University, Xi'an 710051, China; National Key Laboratory of Air Traffic Collision Prevention, Xi'an 710051, China
Di Shen, Air Traffic Control and Navigation School, Air Force Engineering University, Xi'an 710051, China; National Key Laboratory of Air Traffic Collision Prevention, Xi'an 710051, China
Wenda Yang, Air Traffic Control and Navigation School, Air Force Engineering University, Xi'an 710051, China; National Key Laboratory of Air Traffic Collision Prevention, Xi'an 710051, China
Daxiong Chen, PLA 94755 Troops, Zhangzhou 363000, China

Abstract

Abstract: A chaotic ant colony algorithm based on dynamic volatility factor is proposed to solve the problem of multi-aircraft conflict resolution during free flight of fighter jets. The mathematical modelling is conducted on the conflict resolution problem of multiple fighter jets in the air. Based on the performance characteristics of fighter jets, fighter protection zone models, flight conflict models, and resolution models are established respectively. The chaotic ant colony algorithm is improved by using Logistic mapping and Henon mapping to optimize the pheromone update formula in the ant colony algorithm, and setting a dynamic factor for the pheromone volatilization factor to improve search efficiency at different stages. The typical 2-aircrafts, 4-aircrafts, and 6-aircrafts flight conflict scenarios are set up to simulate and verify the effectiveness of the algorithm. The results show that the optimized algorithm is feasible, and all performance indicators of the algorithm are improved.

Recommended Citation

Tong, Liang; Yang, Jie; Gan, Xusheng; Shen, Di; Yang, Wenda; and Chen, Daxiong (2025) "Simulation Research on Multi-aircraft Conflict Resolution Based on Improved Chaotic Ant Colony Algorithm," Journal of System Simulation: Vol. 37: Iss. 1, Article 13.
DOI: 10.16182/j.issn1004731x.joss.23-1117
Available at: https://dc-china-simulation.researchcommons.org/journal/vol37/iss1/13

First Page

155

Last Page

166

CLC

TP391.9

Recommended Citation

Tong Liang, Yang Jie, Gan Xusheng, et al. Simulation Research on Multi-aircraft Conflict Resolution Based on Improved Chaotic Ant Colony Algorithm[J]. Journal of System Simulation, 2025, 37(1): 155-166.

Corresponding Author

Gan Xusheng

DOI

10.16182/j.issn1004731x.joss.23-1117