Optimization of Air Defense System Deployment Against Reconnaissance Drone Swarms
Due to their advantages in flexibility, scalability, survivability, and cost-effectiveness, drone swarms have been increasingly used for reconnaissance tasks and have posed great challenges to their opponents on modern battlefields. This paper studies an optimization problem for deploying air defens...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Tsinghua University Press
2023-06-01
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| Series: | Complex System Modeling and Simulation |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.23919/CSMS.2023.0003 |
| _version_ | 1827093667469328384 |
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| author | Ning Li Zhenglian Su Haifeng Ling Mumtaz Karatas Yujun Zheng |
| author_facet | Ning Li Zhenglian Su Haifeng Ling Mumtaz Karatas Yujun Zheng |
| author_sort | Ning Li |
| collection | DOAJ |
| description | Due to their advantages in flexibility, scalability, survivability, and cost-effectiveness, drone swarms have been increasingly used for reconnaissance tasks and have posed great challenges to their opponents on modern battlefields. This paper studies an optimization problem for deploying air defense systems against reconnaissance drone swarms. Given a set of available air defense systems, the problem determines the location of each air defense system in a predetermined region, such that the cost for enemy drones to pass through the region would be maximized. The cost is calculated based on a counterpart drone path planning problem. To solve this adversarial problem, we first propose an exact iterative search algorithm for small-size problem instances, and then propose an evolutionary framework that uses a specific encoding-decoding scheme for large-size problem instances. We implement the evolutionary framework with six popular evolutionary algorithms. Computational experiments on a set of different test instances validate the effectiveness of our approach for defending against reconnaissance drone swarms. |
| first_indexed | 2024-03-13T03:25:36Z |
| format | Article |
| id | doaj.art-cde42a5581ec48609e25d47248736a20 |
| institution | Directory Open Access Journal |
| issn | 2096-9929 |
| language | English |
| last_indexed | 2025-03-20T06:31:44Z |
| publishDate | 2023-06-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Complex System Modeling and Simulation |
| spelling | doaj.art-cde42a5581ec48609e25d47248736a202024-10-02T08:03:24ZengTsinghua University PressComplex System Modeling and Simulation2096-99292023-06-013210211710.23919/CSMS.2023.0003Optimization of Air Defense System Deployment Against Reconnaissance Drone SwarmsNing Li0Zhenglian Su1Haifeng Ling2Mumtaz Karatas3Yujun Zheng4School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, ChinaCollege of Field Engineering, Army Engineering University, Nanjing 210007, ChinaCollege of Field Engineering, Army Engineering University, Nanjing 210007, ChinaIndustrial Engineering Department, Turkish Naval Academy, National Defense University, Tuzla 34940, TurkeySchool of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, ChinaDue to their advantages in flexibility, scalability, survivability, and cost-effectiveness, drone swarms have been increasingly used for reconnaissance tasks and have posed great challenges to their opponents on modern battlefields. This paper studies an optimization problem for deploying air defense systems against reconnaissance drone swarms. Given a set of available air defense systems, the problem determines the location of each air defense system in a predetermined region, such that the cost for enemy drones to pass through the region would be maximized. The cost is calculated based on a counterpart drone path planning problem. To solve this adversarial problem, we first propose an exact iterative search algorithm for small-size problem instances, and then propose an evolutionary framework that uses a specific encoding-decoding scheme for large-size problem instances. We implement the evolutionary framework with six popular evolutionary algorithms. Computational experiments on a set of different test instances validate the effectiveness of our approach for defending against reconnaissance drone swarms.https://www.sciopen.com/article/10.23919/CSMS.2023.0003drone swarmsanti-droneair defense systemsdeployment optimizationevolutionary algorithms |
| spellingShingle | Ning Li Zhenglian Su Haifeng Ling Mumtaz Karatas Yujun Zheng Optimization of Air Defense System Deployment Against Reconnaissance Drone Swarms Complex System Modeling and Simulation drone swarms anti-drone air defense systems deployment optimization evolutionary algorithms |
| title | Optimization of Air Defense System Deployment Against Reconnaissance Drone Swarms |
| title_full | Optimization of Air Defense System Deployment Against Reconnaissance Drone Swarms |
| title_fullStr | Optimization of Air Defense System Deployment Against Reconnaissance Drone Swarms |
| title_full_unstemmed | Optimization of Air Defense System Deployment Against Reconnaissance Drone Swarms |
| title_short | Optimization of Air Defense System Deployment Against Reconnaissance Drone Swarms |
| title_sort | optimization of air defense system deployment against reconnaissance drone swarms |
| topic | drone swarms anti-drone air defense systems deployment optimization evolutionary algorithms |
| url | https://www.sciopen.com/article/10.23919/CSMS.2023.0003 |
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