Multi-UAV Aerial Mission Intelligent Assignment Algorithm Based on LightGBM Model

Abstract

Abstract With the development of UAV technology, UAVs have the advantages of convenience, speed, and small size. However, when faced with a complex task environment, some traditional artificial intelligence algorithms will significantly increase computation time, and the algorithms that perform better in time performance often fail to adapt to the type and number of random tasks to be assigned. In this paper, we combine the Hungarian algorithm with improving the LightGBM model, simulate the implementation in various task scenarios, and verify that the improved LightGBM model completes the intelligent assignment of multiple UAVs without increasing time cost compared to the classical algorithm through comparative analysis. It is shown that the improved LightGBM model has an average time of 26.39s for 10 runs, which is higher than 16.91s for NSGA-II and 14.73s for PESA, but lower than 46.92s for SPEA2 and 50.01s for SPGA2. For a non-real-time assignment system, a better mission intelligence assignment at a certain time cost scheme is more in line with the system’s performance requirements. Therefore, the improved LightGBM model in this paper can obtain better solutions than the other four algorithms in solving the multi-objective multi-UAV task intelligence assignment problem, and the solution set coverage is larger than several other algorithms. This indicates that the algorithm studied in this paper is feasible in solving the multi-objective multi-UAV task intelligence assignment problem, and it also has some reference significance for solving the multi-objective problem and improving the coverage of the multi-objective solution set.