UAV Task Allocation Based on Clone Selection Algorithm

Zhang, Xiaopan; Chen, Xingjun

doi:10.1155/2021/5518927

Cited by 12 publications

(6 citation statements)

References 23 publications

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“…The MCMUAVCTAP has garnered significant attention from researchers worldwide, resulting in numerous notable advancements. Reference [8] introduced a Clone Selection Algorithm (CSA) designed to optimize four objectives in multi-UAV task allocation: maximizing the number of successfully allocated tasks, maximizing the benefits of executing tasks, minimizing resource costs, and minimizing time costs. Experimental results demonstrated that CSA outperforms the genetic algorithm in multi-objective UAV task allocation.…”

Section: Related Workmentioning

confidence: 99%

A Tent-Lévy-Based Seagull Optimization Algorithm for the Multi-UAV Collaborative Task Allocation Problem

Zhou,

Liu,

Dai

et al. 2024

Applied Sciences

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With the rapid advancement of unmanned aerial vehicle technology, the extensive application of multiple unmanned aerial vehicle systems in agriculture has led to significant innovations and benefits. Addressing the challenge of task allocation for multiple unmanned aerial vehicles, the primary objective is to minimize the total time required for unmanned aerial vehicles to return to their starting point after task completion. To tackle this issue, a mathematical model for the multi-constrained multiple unmanned aerial vehicle collaborative task allocation problem is developed. To efficiently solve this model, we propose an enhanced Seagull Optimization Algorithm, which integrates the Tent chaotic mapping strategy and the Lévy flight strategy. The Tent chaotic mapping helps the algorithm avoid becoming trapped in local optima, while the Lévy flight strategy, employed during the seagull attack phase, enhances the algorithm’s diversity and its ability to escape local optima. Additionally, the spiral coefficient is refined to balance the coordination between global and local searches. Simulation experiments demonstrate that the proposed algorithm can swiftly and effectively identify a reasonable task allocation scheme for solving the multi-constrained multi-UAV collaborative task allocation problem.

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Section: Related Workmentioning

confidence: 99%

A Tent-Lévy-Based Seagull Optimization Algorithm for the Multi-UAV Collaborative Task Allocation Problem

Zhou,

Liu,

Dai

et al. 2024

Applied Sciences

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“…UAV task planning is a problem that finds the optimal path with the minimum cost to complete tasks. These path planning problems are usually solved based on one or several optimization criteria, such as time optimization [3,6,15,18], energy optimization [14,20], time and energy mix optimization [13,22,[25][26][27], and hybrid optimization based on coverage [28]. In terms of time optimization, in Reference [3], a time first immune clonal selection algorithm with optimization modification was proposed to solve the task assignment problem of road patrol.…”

Section: Related Workmentioning

confidence: 99%

“…Then, an improved fruit fly optimization algorithm (ORPFOA) was proposed to solve the path planning problem in the initial task sequence and the new task sequence after task change. In [26], for the solution of task assignment problem, four objectives are simultaneously optimized, namely, maximizing the number of tasks successfully assigned, maximizing task execution benefit, minimizing resource cost and minimizing time cost. A multi-UAV task assignment method based on clone selection algorithm is proposed.…”

Section: Related Workmentioning

confidence: 99%

Multi-UAV Cooperative Assisted RSU Data Acquisition Strategy considering Coverage Quality

Guo

Zhang

et al. 2022

Wireless Communications and Mobile Computing

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The multiple Unmanned Aerial Vehicle (multi-UAV) assisted roadside unit (RSU) data acquisition problem considering the coverage quality is a multiobjective optimization problem, which is a NP-hard problem. Heuristic and hyperheuristic algorithms are effective to solve problems of this type. These algorithms can find the optimal or suboptimal solution in a reasonable time. However, such algorithms still have the problems of low convergence accuracy, slow convergence speed, and being easy to fall into the local optimal solution. In this paper, firstly, according to the specific problem scenarios of roadside unit data collection, minimum cost and maximum coverage models based on task cost and coverage quality are established. Then, to solve the optimization model, combined with the update characteristics of the gray wolf optimization algorithm (GWO) and the whale optimization algorithm (WOA), a hybrid weighted gray wolf and whale optimization algorithm (HWGWOA) is proposed. Finally, to verify the effectiveness of the proposed algorithm, extensive simulation experiments are conducted under four different task acquisition scenarios, and the results are compared with those of genetic algorithm (GA), GWO, and WOA. Simulation results show that the algorithm proposed in this paper not only can get lower task cost and higher coverage quality but also has faster convergence speed and better robustness. Specifically, in terms of task cost, the HWGWOA is about 9.54% lower than the GA, about 7.31% lower than the GWO, and about 5.8% lower than the WOA. In terms of coverage, the HWGWOA is up to about 27.87% higher than the GA, about 15.19% higher than the GWO, and about 9.86% higher than the WOA. Therefore, the algorithm is more suitable for large-scale optimization problems.

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“…The idea of solving it is to construct an assignment model and then calculate the model. To address this problem, a large number of articles has been published, such as mixed integer linear programming [6], simulated annealing [7], particle swarm optimization (PSO) [8], Genetic algorithm [9], and expect system method, etc. Reference [10], starting from the model, uses the expert system method to tackle it.…”

Section: Introductionmentioning

confidence: 99%

Multi-UAV Objective Assignment Using Hungarian Fusion Genetic Algorithm

et al. 2022

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In the background of air combat, the situation between multiple unmanned aerial vehicle (multi-UAV) and objectives has a certain impact on the task assignment. In order to improve the efficiency of assignment and obtain the best assignment scheme during the process of performing tasks, this paper proposes a method to evaluate the situation at a certain time. This method is the basis for establishing a multi-UAV objective assignment model. For solving the model, this paper presents the Hungarian fusion Genetic Algorithm. It first uses the feasible solutions solved by the Hungarian algorithm as the elite individuals in the initial population of the genetic algorithm, and then uses the objective function in the assignment model as the fitness function to optimize the results. The algorithm solves the problem that the assignment result of the Hungarian algorithm is not unique, and optimizes the drawback that the traditional Genetic Algorithm is prone to fall into local optimum. The simulation verified the effectiveness of the situational assessment method and the improved algorithm.

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UAV Task Allocation Based on Clone Selection Algorithm

Cited by 12 publications

References 23 publications

A Tent-Lévy-Based Seagull Optimization Algorithm for the Multi-UAV Collaborative Task Allocation Problem

A Tent-Lévy-Based Seagull Optimization Algorithm for the Multi-UAV Collaborative Task Allocation Problem

Multi-UAV Cooperative Assisted RSU Data Acquisition Strategy considering Coverage Quality

Multi-UAV Objective Assignment Using Hungarian Fusion Genetic Algorithm

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