# GBUO: \"The Good, the Bad, and the Ugly\" Optimizer
## 2 \"The Good, the Bad, and the Ugly\" Optimizer (GBUO)
The exploitation capacity of an optimization algorithm means the ability of that algorithm to achieve and provide a quasi-optimal solution. Therefore, when comparing several optimization algorithms' performance, an algorithm that provides a more appropriate quasi-optimal solution (closer to global optimal) has a higher exploitation capacity than

\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **Test Function** & & **GA** & **PSO** & **GSA** & **TLBO** & **GWO** & **GOA** & **SHO** & **MPA** & **GRUO** \\ \hline \multirow{2}{*}{1} & Unimodal & [PERSON] value & 48 & 47 & 29 & 20 & 18 & 32 & 11 & 28 & **7** \\  & (F1–F7) & [PERSON] rank & 9 & 8 & 6 & 4 & 3 & 7 & 2 & 5 & **1** \\ \hline \multirow{2}{*}{2} & Multimodal & [PERSON] value & 35 & 33 & 27 & 20 & 22 & 34 & 24 & 24 & **9** \\  & (F8–F13) & [PERSON] rank & 8 & 6 & 5 & 2 & 3 & 7 & 4 & 4 & **1** \\ \hline \multirow{3}{*}{3} & Fixed-dimension multimodal & [PERSON] value & 54 & 43 & 37 & 33 & 30 & 34 & 52 & 21 & **10** \\  & (F14–F23) & [PERSON] rank & 9 & 7 & 6 & 4 & 3 & 5 & 8 & 2 & **1** \\ \hline \multirow{3}{*}{4} & All 23-test functions & [PERSON] value & 137 & 123 & 93 & 73 & 70 & 100 & 87 & 73 & **26** \\ \cline{2-11} \cline{



other algorithms. An optimization algorithm's exploration capacity means that the algorithm's ability to accurately scan the search space, solving optimization problems with several local optimal solutions; the exploration capacity has a considerable effect on providing a quasi-optimal solution. In such problems, if the algorithm does not have the appropriate exploration capability, it provides non-optimal solutions by getting stuck in optimal local locals. The unimodal objective functions \(F_{1}\) to \(F_{7}\) are functions that have only one global optimal solution and lack local optimal local. Therefore, this set of objective functions is suitable for analyzing the exploitation capacity of the optimization algorithms. Table 3 presents the results obtained from implementing the proposed GBUO and eight other optimization algorithms on the unimodal objective functions in order to properly evaluate the exploitation capacity. Evaluation of the results shows that the proposed optimizer provides more suitable quasi-optimal solutions than the other eight algorithms for all \(F_{1}\) to \(F_{7}\) objective functions. Accordingly, GBUO has a high exploitation capacity and is much more competitive than the other mentioned algorithms. The second (\(F_{8}\) to \(F_{13}\)) and the third (\(F_{14}\) to \(F_{23}\)) categories of the objective functions have several local optimal solutions besides optimal solutions. Therefore, these types of objective functions are suitable for analyzing the exploration capability of the optimization algorithms. Tables 4 and 5 present the results of implementing the proposed GBUO and eight other optimization algorithms on the multimodal objective functions to tolerate capability. The results presented in these tables show that the proposed GBUO has a good exploration capability. Moreover, the proposed GBUO can also find local-optimal solutions by accurately scanning the search space and thus, does not get stuck in local optimal to the other eight algorithms. The performance of the proposed GBUO is more appropriate and competitive for solving this type of optimization problem. It is confirmed that GBUO is a useful optimizer for solving different types of optimization problems. ## 5 Conclusions

In this paper, a new optimization method called \"the good, the bad, and the ugly\" optimizer (GBUO) has been introduced based on the effect of three members of the population on population updating. These three influential members include the good member with the best value of the objective function, the bad member with the worst value of the objective function, and the ugly member selected randomly.