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Open Access

Optimization of Air Defense System Deployment Against Reconnaissance Drone Swarms

School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China
College of Field Engineering, Army Engineering University, Nanjing 210007, China
Industrial Engineering Department, Turkish Naval Academy, National Defense University, Tuzla 34940, Turkey
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Abstract

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.

Keywords

drone swarmsanti-droneair defense systemsdeployment optimizationevolutionary algorithms

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Complex System Modeling and Simulation
Volume 3 Issue 2,
June 2023
Pages 102-117
DOI: 10.23919/CSMS.2023.0003
Cite this article:
Li N, Su Z, Ling H, et al. Optimization of Air Defense System Deployment Against Reconnaissance Drone Swarms. Complex System Modeling and Simulation, 2023, 3(2): 102-117. https://doi.org/10.23919/CSMS.2023.0003

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Received: 25 November 2022
Revised: 22 December 2022
Accepted: 29 January 2023
Published: 20 June 2023
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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