# Improvement of Dung Beetle Optimization Algorithm Application to Robot Path Planning
## 4 Results of the Experiment and Discussion
### Comparison with Other Algorithms
Comparison between TSDBO and MSADBO: The average convergence accuracy of TSDBO converges to the theoretical optimal solution on F1 and F3, while the average optimization accuracy of MSADBO only converges to the theoretical optimal solution on F1, and MSADBO, like most of the other optimization algorithms, falls into the local optimal solution in the function F5, where finding the best is more difficult, whereas the optimal value of TSDBO approaches the theoretical best value of 0, with an average convergence accuracy of 0. The theoretical optimal solution is 0, and the average convergence accuracy also reaches 2.34E\(-\)01, which is better than that of MSADBO; on the multi-peak functions F6-F9, TSDBO shows superior performance in searching for the optimal solution, which indicates that TSDBO is quite good at both local escape and global search; on the hybrid benchmark test functions F10-F12, the optimal values of TSDBO and MSADBO have the same optimal values, but are better than MSADBO in both average convergence accuracy and standard deviation. When analyzed together, TSDBO's optimality-seeking performance on its test functions is stronger than MSADBO. 5. Comparison between TSDBO and MSDBO: Compared to IDBO and GODBO, MSDBO performs better overall, and it can find the theoretical best value in the F7 and F10 functions, but the Mean and Std are worse than those of TSDBO, and the optimization results are not as accurate as those of TSDBO on other functions. 6. Comparison between TSDBO and IWOA: IWOA performs similarly to GODBO on the test functions and has no competitive advantage over TSDBO. 7. Comparison between TSDBO and SAGWO: SAGWO outperforms GODBO and IWOA in F1-F4, has a good performance in the F6 function, and finds the theoretical optimal value in the F7, F9, and F10 functions, but the Mean and Std are not comparable to those of TSDBO, and the overall performance is not as good as TSDBO. In summary, TSDBO has a decent performance in the comparison algorithms, with a combined performance of first place in each function. The introduction of the first strategy improves the initial population state of the algorithm and lays a solid foundation for the subsequent steps of the algorithm. The introduction of the second strategy improves the exploration and development of the algorithm dramatically, which can be explored more fully and the performance of the algorithm can be utilized. The introduction of the third strategy not only accelerates the convergence speed of the algorithm to a certain extent, but also allows the algorithm to jump out of the local optimum solution when it falls into the local optimum, so that the performance of the algorithm can be completely released. To better help the reader understand the data in Table 3, we give the number of times each algorithm appears to be optimal in each metric in Figure 6. It can be clearly seen that  
functions, TSDBO is able to start the iterative optimization search from the neighborhood close to the theoretical value, which not only greatly reduces the number of iterations, but also enables the computer's arithmetic power to be fully used. This is a good indication that the introduction of the three strategies is successful and can perform even better on top of the excellent performance of the base algorithm.