# Improvement of Dung Beetle Optimization Algorithm Application to Robot Path Planning
## 4 Results of the Experiment and Discussion
### Comparison with Other Algorithms  
The TSDBO algorithm is compared against other enhanced algorithms in order to confirm its efficacy, including the Salp Swarm Algorithm (SSA) [41], the Dung Beetle Optimization Algorithm (IDBO) [42] with the introduction of a cycle mutation mechanism, the Multi-Strategy Dung Beetle Optimization Algorithm (GODBO) [43] based on Compositional Learning (OBL), the Sinusoidal Algorithm-Directed Dung Beetle Optimization Algorithm (MSADBO) [44], the Multi-Strategy Improved Dung Beetle Optimization Algorithm (MSDBO) [45], the Improved Whale Optimization Algorithm (IWOA) [46], and the Gray Wolf Optimization Algorithm with Adaptive Search Strategy (SAGWO) [47]. The number of pertinent runs is still fixed at 500 iterations and 30 populations for the accuracy of the results. All algorithms' other parameters were rigorously established in accordance with the original text. The optimization results of each method operating independently for 30 runs are displayed in Table 3 along with the Best, Mean, and Std. To make it easier to read, TSDBO's data have been bolded by us. Figure 5: Iteration convergence curve for different strategies and improved algorithm. The TSDBO method reaches the theoretical ideal value of 0 in the F1-F3 functions. Out of the five single-peak benchmark functions (F1-F5), TSDBO performs better than the other six comparison algorithms in the F1-F4 function across all assessment measures. Finding the ideal value of single-peak functions is more challenging for the F5 function, and most functions' optimal outcomes range around 25. According to Table 3's statistics, the TSDBO algorithm's optimization results can approach the F5 function's optimal value, and its average value is lower than that of the other methods under comparison. The TSDBO method outperforms the seven algorithms in the comparison and ranks first out of the various algorithms when the combined overall performance of F1-F5 is taken into account. In the four multi-peak benchmarking functions (F6-F9), the TSDBO can find the theoretical optimal value Best in F6, and the mean (Mean) is the smallest among several algorithms; in the F7 function, the TSDBO and MSADBO are tied for the first place. In the F8 function, the TSDBO and a number of additional algorithms are able to determine the theoretically ideal value, but the TSDBO's Std value is smaller. In the F9 function, the TSDBO outperforms several other algorithms. Various approaches can locate the optimum or the optimization results can be near the theoretical optimum in the three hybrid benchmark test functions (F10-F12). The TSDBO method outperforms a number of other algorithms in the F10 function, while its Std value is lower than the GODBO algorithm's. The F11 function's Best, Mean, and Std values are lowest for the TSDBO algorithm, which also has the greatest rating. All of the comparison algorithms discover the theoretically ideal value of Best in the F12 function; however, the TSDBO algorithm's Mean and Std values outperform those of the other techniques.