Adaptive ant colony optimization integrated with dynamic risk mapping for tactical vehicle path planning in dynamic battlefields
Keywords:
Adaptive ACO, Adaptive Navigation, Battlefield Simulation, Combat Vehicles, Dynamic Risk MapAbstract
The movement of combat vehicles in modern battlefields faces complex challenges in the form of uncertain terrain, dynamic enemy threats, and limited real-time information, making conventional methods such as Dijkstra or A* less capable of optimising routes adaptively. This research aims to develop an Adaptive Ant Colony Optimization (ACO) algorithm model integrated with a dynamic risk map to determine safe, fast, and efficient routes for combat vehicles. The methodology employed includes designing an adaptive ACO with risk-based pheromone update mechanisms, modeling dynamic risk maps using Gaussian probability functions and Markov models, and conducting graph-based battlefield simulations to evaluate algorithm performance. Evaluation was conducted by comparing the adaptive ACO with baseline algorithms (Dijkstra, A*, and Particle Swarm Optimization) using metrics such as Safety Index (SI), Time Efficiency (TE), Adaptability, and Computational Cost (CC). The results show that the adaptive ACO consistently produces paths with the highest SI values, competitive time efficiency, and better real-time adaptability compared to the baseline, while path visualization demonstrates the algorithm's ability to dynamically avoid high-risk areas. These findings indicate that integrating adaptive ACO with dynamic risk maps provides safer and more flexible navigation strategies, with significant potential for application in autonomous combat vehicles, UAV systems, and military operations based on intelligent simulation. This research contributes to the development of adaptive path optimization algorithms in dynamic battlefields, bridges the literature gap related to risk-based navigation, and provides a framework that can serve as the foundation for developing military decision support systems based on artificial intelligence.
References
Abbasi, M. A., Amran, A., Khan, R., & Sahar, N. E. (2024). Linking corporate social irresponsibility to workplace deviant behavior: A comparative analysis of generation Z and Generation Y. Current Psychology, 43(2), 1118–1135. https://doi.org/10.1007/s12144-023-04372-z
Ahmadi, K. D., Rashidi, A. J., & Moghri, A. M. (2022). Design and simulation of autonomous military vehicle control system based on machine vision and ensemble movement approach. Journal of Supercomputing, 78(15), 17309–17347. https://doi.org/10.1007/s11227-022-04565-6
Ayeni, O. (n.d.). Advanced Multi-Phase Project Management Frameworks: Optimizing AI-Driven Decision-Making, Risk Control, and Efficiency.
Aziz, A., Tasfia, S., & Akhtaruzzaman, M. (2022). A Comparative Analysis among Three Different Shortest Path-finding Algorithms. 2022 3rd International Conference for Emerging Technology, INCET 2022, 1–4. https://doi.org/10.1109/INCET54531.2022.9824074
Bhavya, R., & Elango, L. (2023). Ant-inspired metaheuristic algorithms for combinatorial optimization problems in water resources management. Water, 15(9), 1712.
Brown, N., South, K., & Wiese, E. S. (2022). The Shortest Path to Ethics in AI: An Integrated Assignment Where Human Concerns Guide Technical Decisions. ICER 2022 - Proceedings of the 2022 ACM Conference on International Computing Education Research, 1, 344–355. https://doi.org/10.1145/3501385.3543978
Chen, G., Wu, T., & Zhou, Z. (2021). Research on Ship Meteorological Route Based on A-Star Algorithm. Mathematical Problems in Engineering, 2021(1), 9989731. https://doi.org/10.1155/2021/9989731
Deng, W., Xu, J., & Zhao, H. (2019). An Improved Ant Colony Optimization Algorithm Based on Hybrid Strategies for Scheduling Problem. IEEE Access, 7, 20281–20292. https://doi.org/10.1109/ACCESS.2019.2897580
Dorigo, M., & Stützle, T. (2003). The ant colony optimization metaheuristic: Algorithms, applications, and advances. Handbook of Metaheuristics, 250–285.
Dorigo, M., & Stützle, T. (2019). Ant colony optimization: Overview and recent advances. International Series in Operations Research and Management Science, 272, 311–351. https://doi.org/10.1007/978-3-319-91086-4_10
Fahmi, H., Zarlis, M., Nababan, E. B., & Sihombing, P. (2020). Ant colony optimization (ACO) algorithm for determining the nearest route search in distribution of light food production. Journal of Physics: Conference Series, 1566(1), 12045.
Fedorovych, O., Kritskiy, D., Malieiev, L., Rybka, K., & Rybka, A. (2024). Military Logistics Planning Models for Enemy Targets Attack By a Swarm of Combat Drones. Radioelectronic and Computer Systems, 2024(1(109)), 207–216. https://doi.org/10.32620/REKS.2024.1.16
Goje, B., Nayak, P., & Hanuman, A. S. (2022). An Optimal Route Finding on Road Networks Using A*, Dijkstra & Bidirectional Algorithms: A Brief Comparison. 2022 13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022, 1–7. https://doi.org/10.1109/ICCCNT54827.2022.9984394
Hasanov, A., Tahirov, R., & Iskandarov, K. (2024). The future of warfare. Anticipated changes in military trends. Journal of Scientific Papers “Social Development and Security,” 14(5), 1–18. https://doi.org/10.33445/sds.2024.14.5.1
Hu, W., Wu, K., Shum, P. P., Zheludev, N. I., & Soci, C. (2016). All-Optical Implementation of the Ant Colony Optimization Algorithm. Scientific Reports, 6(1), 26283. https://doi.org/10.1038/srep26283
Li, B., Hou, J., Wang, X., Ma, Y., Li, D., Wang, T., & Chen, G. (2023). High-Resolution Flood Numerical Model and Dijkstra Algorithm Based Risk Avoidance Routes Planning. Water Resources Management, 37(8), 3243–3258. https://doi.org/10.1007/s11269-023-03500-5
Mustafa, H. M. H., Al-Ghamdi, S. A., & Al-Shenawy, N. M. (2015). Comparative Performance Analysis and Evaluation for One Selected Behavioral Learning System versus an Ant Colony Optimization System. The Second International Conference on Electrical, Electronics, Computer Engineering and Their Applications (EECEA2015), 27.
Sohrabi, S., & Lord, D. (2022). Navigating to safety: Necessity, requirements, and barriers to considering safety in route finding. Transportation Research Part C: Emerging Technologies, 137, 103542. https://doi.org/10.1016/j.trc.2021.103542
Song, C., Jia, Y., Wang, H., & Yang, W. (2024). Comprehensive overview of key technologies in US Army unmanned combat vehicles. Journal of Engineering Systems, 2(1), 101–106.
Yan, F. (2018). Autonomous vehicle routing problem solution based on artificial potential field with parallel ant colony optimization (ACO) algorithm. Pattern Recognition Letters, 116, 195–199.
Yevsieiev, V., Abu-Jassar, A., & Maksymova, S. (2024). Building a traffic route taking into account obstacles based on the A-star algorithm using the python language.
Zhang, H., Wang, X., Memarmoshrefi, P., & Hogrefe, D. (2017). A survey of ant colony optimization based routing protocols for mobile ad hoc networks. IEEE Access, 5, 24139–24161.
Zhu, W., Cai, W., & Kong, H. (2025). Optimal path planning based on ACO in intelligent transportation. International Journal of Cognitive Computing in Engineering, 6, 441–450.
