Multi-UAV Task Assignment Based on Improved Hybrid Frog Hopping Algorithm
Baojun Zhang, Lei Lei · 2024
Aiming at the problems of low algorithm search efficiency, weak performance, slow convergence speed and easy to fall into local optimization in the multi-UAV task allocation problem, an improved hybrid frog leaping algorithm, also known as (Improve Shuffled Frog Leaping Algorithm, ISFLA) is proposed. On the basis of analyzing the task allocation model, the basic Shuffled Frog Leaping Algorithm (SFLA) is improved by introducing a reverse learning strategy and a firework explosion mechanism, which can effectively improve the diversity of the population; introducing a neighborhood learning strategy for the optimal individual, a dynamic jumping strategy for the worst individual, and introducing a Levy flight factor, which can effectively avoid the algorithm from falling into a local optimum early. The introduction of neighbor learning strategy for the optimal individuals, dynamic jumping strategy for the worst individuals and Levy flight factor can effectively avoid the algorithm from falling into the local optimum too early, and improve the search performance and convergence speed. The auction mechanism is utilized to cope with the dynamic task allocation in case of the unexpected situation when multiple UAVs perform the task cooperatively, which further improves the convergence performance of the algorithm. Simulation verification is carried out in different environment scales. Comparison results show that the improved hybrid frog jump algorithm improves the convergence speed and search accuracy in the multi-UAV task allocation problem.