Abstract: The dung beetle optimizer (DBO) is a novel global optimization meta-heuristic algorithm characterized by its strong optimization capability and fast convergence speed. However, it also has drawbacks such as being prone to local optima and low convergence accuracy. To address these issues, this paper proposes a hybrid strategy improved dung beetle algorithm (HSIDBO). Firstly, an improved Logistic chaos is used for population initialization to obtain a more uniformly distributed population. Secondly, an adaptive optimal guidance strategy is adopted to increase the algorithms convergence speed and local contraction ability. Finally, a lens imaging learning strategy is introduced to improve the dung beetles theft process, thereby enhancing the algorithm's local escape ability. Tests were conducted on 14 classic benchmark functions and engineering application problems. The results demonstrate that the integration of these three strategies can effectively enhance the performance of the dung beetle optimizer.

Key words: dung beetle optimization algorithm, Chaos napping, optimal guidance, lens imaging learning