Abstract
With the continuous development of computer and network technology, the large-scale and clustered operations of drones have gradually become a reality. How to realize the reasonable allocation of UAV cluster combat tasks and realize the intelligent optimization control of UAV cluster is one of the most challenging difficulties in UAV cluster combat. Solving the task allocation problem and finding the optimal solution have been proven to be an NP-hard problem. This paper proposes a CSA-based approach to simultaneously optimize four objectives in multi-UAV task allocation, i.e., maximizing the number of successfully allocated tasks, maximizing the benefits of executing tasks, minimizing resource costs, and minimizing time costs. Experimental results show that, compared with the genetic algorithm, the proposed method has better performance on solving the UAV task allocation problem with multiple objectives.
Highlights
With the rapid development of Internet of Things and 5G communication technologies, UAV systems are increasingly used in the military field and UAV operations have become an important part of modern military operations
Many methods for solving the task allocation problem have been proposed, which can be roughly divided into four categories: graph theory [1], integer linear programming [2], state space search [3], and Artificial Intelligence (AI) methods such as genetic algorithm, particle swarm algorithm, simulated annealing, and ant colony algorithm [4,5,6,7,8]
This paper proposes to use clone selection algorithm (CSA) to optimize four objectives in UAV task allocation, i.e., maximizing the number of successfully assigned tasks, maximizing the benefits of executing tasks, minimizing resource cost, and minimizing time cost, and comprehensively considers the time constraints, resource constraints, and functional constraints in real-world scenarios
Summary
With the rapid development of Internet of Things and 5G communication technologies, UAV systems are increasingly used in the military field and UAV operations have become an important part of modern military operations. Most methods in the first three categories are complete search algorithms These algorithms can get the optimal solution, but they require a lot of computing resources and time cost, and it is impractical to apply them to a large-scale problem. A clone selection algorithm (CSA) is proposed based on related immune principles [12,13,14] This algorithm is widely used in function optimization [15] (e.g., multimodal optimization and continuous function optimization), pattern recognition [16, 17] (e.g., binary character and face recognition), and scheduling problems [18]. Comparing with the brute-force search algorithm and genetic algorithm, experimental results show that the proposed method could achieve better performance on solving high-dimensional multiobjective task allocation problems.
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