Abstract: Taking inspiration from the leader follower strategy, a dung beetle optimization algorithm based on neighborhood search strategy is proposed to address the problems of slow convergence speed, easy falling into local optima, and weak global exploration ability in the optimization algorithm. Firstly, introducing Singer mapping to initialize the population improves the quality of initial solutions and enhances the convergence speed of the algorithm; Secondly, a neighborhood search strategy is proposed to enhance population diversity, break away from local convergence, and improve the local development ability of the algorithm; Finally, an elite pool perturbation strategy is designed to expand the search range, enhance the algorithm’s global exploration and local optimization capabilities, and improve the algorithm’s solving efficiency and accuracy. In order to verify the effectiveness of the proposed algorithm, a series of experiments are designed in this paper to verify its performance. The results indicate that the algorithm has significantly improved optimization accuracy and convergence speed. The algorithm is applied to the three-dimensional path planning problem of unmanned aerial vehicles, and the experimental results show that the algorithm demonstrates effectiveness and efficiency in dealing with practical application problems.

Key words: dung beetle optimization algorithm, path planning, singer mapping, neighborhood search strategy, elite pool-disturbance strategy