Abstract: In the context of the rapid development of UAV technology,efficient path planning strategies have become the key to improve the effectiveness and safety of UAV mission execution.In this paper,a multi-strategy dung beetle optimization(MDBO) algorithm is proposed to tackle this issue.The MDBO algorithm incorporates the Latin hypercubic sampling initialization strategy,mean difference variational strategy and the fusion lens imaging backward learning and dimension-by-dimension optimization strategies,it significantly improves the convergence accuracy and convergence speed of the algorithm,and enhances the global optimization capability.The MDBO is compared with the DBO,COA and GWO algorithms on the UAV path planning problem through MATLAB simulation experiments.And the experimental results demonstrate that for the two constructed maps,the average value of the flight path length solved by MDBO is reduced by 5.1% and 5.9% compared with DBO,and it has good convergence speed and stability,which verifies the effectiveness and superiority of the improved algorithm.

Key words: UAV path planning, Swarm intelligence, Dung beetle optimization algorithms, Latin hypercube sampling, Mean diffe-rence variance