Improved genetic algorithm based 3-D deployment of UAVs

Wen, Xiting; Ruan, Yuhan; Li, Yongzhao; Xia, Huang; Zhang, Rui; Wang, Chao; Liu, Wei; Jiang, Xiaoyu

doi:10.23919/jcn.2022.000014

Cited by 26 publications

(8 citation statements)

References 22 publications

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“…However, traditional GAs still face issues like slow convergence and susceptibility to local optima when dealing with multi-UAV cooperative task allocation problems. To overcome these shortcomings, researchers have begun to explore improved GAs for solving multi-UAV cooperative task allocation problems [13]. These improvements include the introduction of adaptive strategies, multi-objective optimization, and local search methods, aiming to enhance the algorithm's global search capability and convergence speed.…”

Section: Related Workmentioning

confidence: 99%

Cooperative task allocation method for multi‐unmanned aerial vehicles based on the modified genetic algorithm

Tan,

Zhou,

Qian

2024

IET Intelligent Trans Sys

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Unmanned aerial vehicles (UAVs) play a crucial role in various domains such as military, civil, industrial, and so on. However, the coordination and task allocation of multiple UAVs in engineering practices face numerous challenges. As the scale of battlefields expands, and the diversity of UAV missions and constraints increases, the existing task allocation methods suffer from issues such as a mismatch between theoretical models and real‐world applications, low task execution efficiency, and poor responsiveness in dynamic environments. To address these challenges, this paper proposes an improved genetic algorithm (GA)‐based approach for multi‐UAV cooperative task allocation. By collecting battlefield information, decomposing tasks, and considering UAV resource types, an optimization model for multi‐UAV cooperative task allocation is constructed. The proposed method, using an improved GA, generates a set of Pareto‐optimal task solutions for decision‐makers. Case studies demonstrate that this approach effectively enhances task execution efficiency and reduces the total flight distance cost of UAVs.

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Section: Related Workmentioning

confidence: 99%

Cooperative task allocation method for multi‐unmanned aerial vehicles based on the modified genetic algorithm

Tan,

Zhou,

Qian

2024

IET Intelligent Trans Sys

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“…In particular, in [77], a GA approach for the shortest path problem under two different settings has been proposed. Also in [78], the network design problem with the goal of optimizing vehicle travel distance has been studied with GA, while unmanned aerial vehicle (UAV) networks [79], and energy-efficient resource allocation [80] have been studied using GA. digital data service (DDS), which is a famous communication service, is studied in [81] where the authors proposed a GA for the Steiner-tree problem that is tightly connected to the design of DDS networks.…”

Section: Genetic Algorithmmentioning

confidence: 99%

HUB-GA: A heuristic for universal lists broadcasting using genetic algorithm

Gholami

Harutyunyan

2023

J. Commun. Netw.

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“…Metaheuristic algorithms are widely used to solve problems related to power system optimization because of their simple structure, few adjustment parameters, and lack of need for gradient information. Wen et al (2022) proposed a novel heuristic algorithm based on a three-dimensional (3D) UAV deployment scheme that could be used by a number of covered users without increasing the number of UAVs. Fan et al (2022b) proposed an improved RRT algorithm based on the process of extending the random tree, and introduced ACO to make the planning path asymptotically optimal.…”

Section: Introductionmentioning

confidence: 99%

UAV trajectory planning based on an improved sparrow optimization algorithm with multi-strategy integration

Yang

2022

Front. Environ. Sci.

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Real-time monitoring of urban high-altitude data is an important goal in the construction and development of smart cities today. However, with the development of modern cities, the monitoring space becomes complicated and narrow because of the different building heights and no-fly zones, which makes UAV trajectory planning more difficult. In this paper, a multi-strategy sparrow search algorithm (MSSA) is proposed to solve the UAV trajectory planning problem in a three-dimensional environment. The algorithm aims to minimize the flight distance and maximize the use efficiency of the UAV. First, the improved algorithm employed a reverse-learning strategy based on the law of refraction to improve the search range and enhance the optimization performance. Second, we introduced a random step size generated by Levy flight into the position update strategy of the participant. The algorithm accuracy and speed of convergence were improved by the randomness feature. Finally, the algorithm incorporated the Cauchy mutation to improve the scout position, which enhanced its ability to jump out of the local optimum of the algorithm. Sixteen benchmark test functions, Wilcoxon rank sum test, and 30 CEC2014 test function optimization results demonstrated that MSSA had better optimization accuracy, convergence speed, and robustness than the comparison algorithms. In addition, the proposed algorithm was applied to the UAV trajectory planning problem in different complex 3D environments. The results confirmed that the MSSA outperformed the other algorithms in complex 3D trajectory planning problems.

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Improved genetic algorithm based 3-D deployment of UAVs

Cited by 26 publications

References 22 publications

Cooperative task allocation method for multi‐unmanned aerial vehicles based on the modified genetic algorithm

Cooperative task allocation method for multi‐unmanned aerial vehicles based on the modified genetic algorithm

HUB-GA: A heuristic for universal lists broadcasting using genetic algorithm

UAV trajectory planning based on an improved sparrow optimization algorithm with multi-strategy integration

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