Large-scale UAV swarm confrontation based on hierarchical attention actor-critic algorithm

Nian, Xiaohong; Li, Mengmeng; Wang, Haibo; Gong, Yalei; Xiong, Hongyun

doi:10.1007/s10489-024-05293-5

Cited by 5 publications

(1 citation statement)

References 21 publications

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“…Furthermore, due to limitations of space, we cannot conduct comparative study with more existing works within this paper. We may further do this in the future with, say, [47][48][49][50][51], which involves RL-based methods, visual language models, game-theoretic approaches, and evolutionary strategies. Additionally, it is necessary to further investigate the dynamics of swarm confrontation under conditions of incomplete information in order to enhance the broader applicability of the bio-inspired confrontation algorithm.…”

Section: Discussionmentioning

confidence: 99%

Bio-Inspired Intelligent Swarm Confrontation Algorithm for a Complex Urban Scenario

Cai,

Luo,

Gao

et al. 2024

Electronics

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This paper considers the confrontation problem for two tank swarms of equal size and capability in a complex urban scenario. Based on the Unity platform (2022.3.20f1c1), the confrontation scenario is constructed featuring multiple crossing roads. Through the analysis of a substantial amount of biological data and wildlife videos regarding animal behavioral strategies during confrontations for hunting or food competition, two strategies are been utilized to design a novel bio-inspired intelligent swarm confrontation algorithm. The first one is the “fire concentration” strategy, which assigns a target for each tank in a way that the isolated opponent will be preferentially attacked with concentrated firepower. The second one is the “back and forth maneuver” strategy, which makes the tank tactically retreat after firing in order to avoid being hit when the shell is reloading. Two state-of-the-art swarm confrontation algorithms, namely the reinforcement learning algorithm and the assign nearest algorithm, are chosen as the opponents for the bio-inspired swarm confrontation algorithm proposed in this paper. Data of comprehensive confrontation tests show that the bio-inspired swarm confrontation algorithm has significant advantages over its opponents from the aspects of both win rate and efficiency. Moreover, we discuss how vital algorithm parameters would influence the performance indices.

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Section: Discussionmentioning

confidence: 99%

Bio-Inspired Intelligent Swarm Confrontation Algorithm for a Complex Urban Scenario

Cai,

Luo,

Gao

et al. 2024

Electronics

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UAV Swarm Objectives: A Critical Analysis and Comprehensive Review

Kumar,

Manoj,

Sudheer

et al. 2024

SN COMPUT. SCI.

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UAV swarm path planning approach based on integration of multi-population strategy and adaptive evolutionary optimizer

Wang,

Hu,

Gao

et al. 2024

Meas. Sci. Technol.

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Addressing the optimal path planning problem encountered by swarm of unmanned aerial vehicle (UAV) in three-dimensional space under multiple constraints, the Multi-population Adaptive Cuckoo Search and Grey Wolf Optimizer (MACSGWO) integrates Multi-Population (MP) strategies and adaptive evolutionary optimizer including the enhanced Adaptive Grey Wolf Optimizer (AGWO) and adaptive Cuckoo search (ACS). The optimizer strategically divides the initial population into multiple sub-groups, enabling each sub-group to independently iterate. During the iteration process, the algorithm adaptively adjusts parameters based on the optimal fitness values obtained by each sub-group after each iteration. The iteration cycle is divided into two stages: during the global exploration phase, each sub-group autonomously executes AGWO and periodically shares the fitness information of the Alpha wolf with other sub-groups, accelerating convergence. In the subsequent local optimization phase, MACSGWO dynamically decides whether to initiate ACS based on the disparity in the best fitness of each sub-group after each iteration, assisting the algorithm in escaping local optima. In experiments involving various complex benchmark functions and swarm path planning scenarios, MACSGWO demonstrated significant superiority in solution stability, convergence speed, and optimal convergence value compared to multiple existing variant algorithms. The integration of MACSGWO with the best relay UAV selection strategy further optimized the communication efficiency within the swarm. MACSGWO ensures the efficient resolution of UAV swarm path planning problems, providing robust support for optimization challenges in complex, multi-constraint scenarios.

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Large-scale UAV swarm confrontation based on hierarchical attention actor-critic algorithm

Cited by 5 publications

References 21 publications

Bio-Inspired Intelligent Swarm Confrontation Algorithm for a Complex Urban Scenario

Bio-Inspired Intelligent Swarm Confrontation Algorithm for a Complex Urban Scenario

UAV Swarm Objectives: A Critical Analysis and Comprehensive Review

UAV swarm path planning approach based on integration of multi-population strategy and adaptive evolutionary optimizer

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