## 5 Experimental Results and Discussions  
To demonstrate the effectiveness and efficiency of our proposed method, SMOBHC, we conducted tests on three battlefields, each containing ten, thirty, and twenty threats distributed randomly under specific conditions. The algorithms used in the tests were run on a PC with an Intel(R) Core (TM) i7-4510U CPU at 2.60 GHz, 6 GB RAM, and 64-bit Windows 10 operating system. The algorithm codes were compiled using MATLAB 2022b. The centers and radii of all threats in the three battlefields were defined as follows:  
Battlefield 1:  
\[X^{1}_{\text{threat }center}=[350,\ 105,\ 305,\ 105,175,\ 245,\ 415,\ 480,\ 40,\ 4 70],\]  
\[Y^{1}_{\text{threat }center}=[200,-25,-150,\ 95,\ 55,\ 60,\ 0,\ 110,\ 100,-50 ],\]  
\[\text{Radius}^{1}_{\text{threat }center}=[140,\ 70,\ 150,\ 30,\ 25,\ 25,\ 25,\ 9 0,\ 40,\ 30 ].\]  
Battlefield 2:  
\[X^{2}_{\text{threat }center_{j}}=\Big{[}X^{1}_{\text{threat }center}\,X^{1}_{\text{threat }center}+500,\ X^{1}_{\text{threat }center}+1000\Big{]},\ \text{where}\ j=1,...30,\]  
\[Y^{2}_{\text{threat }center_{j}}=\Big{[}Y^{1}_{\text{threat }center}\,Y^{1}_{\text{threat }center},Y^{1}_{\text{threat }center}\Big{]},\ \text{where}\ j=1,...30,\]  
Figure 7: Graphical representation of UCAV Path planning based on SMOBHC. \[\mathrm{Radius}_{\mathrm{threat\ center}_{j}}^{2}=.\Big{[}\mathrm{Radius}_{\mathrm{threat\ center}}^{1},\mathrm{Radius}_{ \mathrm{threat\ center}}^{1},\mathrm{Radius}_{\mathrm{threat\ center}}^{1}\Big{]},\ \mathrm{where}\ j=1,...30,\] Battfield 3: \[X_{\mathrm{threat\ center}_{j}}^{3}=\big{[}X_{\mathrm{threat\ center}}^{1},X_{ \mathrm{threat\ center}}^{1}+500\big{]},\ \mathrm{where}\ j=1,...20,\] \[Y_{\mathrm{threat\ center}_{j}}^{3}=\big{[}Y_{\mathrm{threat\ center}}^{1},Y_{ \mathrm{threat\ center}}^{1}\big{]},\ \mathrm{where}\ j=1,...20,\] \[\mathrm{Radius}_{\mathrm{threat\ center}_{j}}^{3}=.\Big{[}\mathrm{Radius}_{\mathrm{threat\ center}}^{1},\mathrm{Radius}_{ \mathrm{threat\ center}}^{1}\Big{]},\ \mathrm{where}\ j=1,...20,\]  
The starting and terminal nodes for the three battlefields are positioned at points \(S\) and \(T\), which have coordinates \(S^{1}(0,0)\), \(T^{1}(500,0)\), \(S^{2}(0,0)\), \(T^{2}(1000,0)\) and \(S^{3}(10,10)\), \(T^{1}(950,0)\), respectively. Additionally, the length of the segment \(ST\) for each battlefield is calculated using the following equation:  
\[Y^{1}_{\text{threat }center}=[200,-25,-150,\ 95,\ 55,\ 60,\ 0,\ 110,\ 100,-50 ],\]0  
In this study, the SMOBHC is benchmarked using three battlefields under three different problem dimensions of 30, 60, and 90. The control parameters for SMOBHC are set as follows: the size of the population \(N\) is 50, the maximum group limit (\(MG\)) is 2, the initial group number (GroupNumber) is 1, \(LLL=N\ x\ D\), \(GLL=60\), \(pr=0.8\), \(\mathcal{N}\)-operator bandwidth (\(b_{w}\)) is 0.001, and \(\mathcal{B}\)-operator probability (\(\mathcal{B}\)) is 0.1. The SMOBHC is evaluated with a maximum of 1000 iterations (\(Iter_{max}\)) and run 20 times (\(Run_{max}\)). The best, worst, mean, and standard deviation (std.) of all SMOBHC results are calculated. The SMOBHC algorithm is compared to SMO and 13 other algorithms for the first two battlefields under the same conditions. These algorithms are PSO, GQPSO [46], BA [47], CS [48], DE, FA [49], GCMBO [50], GWO [51], HS [52], WOA [53], ALO [54], AOA [55], and DA [56]. For the third battlefield, it is compared to SMO and other algorithms, such as AHA [57], AOS [58], ARO [59], SO [60], BWO [61], SCO [62], GBO [63], DMOA [64], DO [65], EO [66], FHO [67], WSO [68], and POA [69]. The results of the first two tests (battlefields 1 and 2) obtained using all 15 algorithms are shown in Tables 3-6, and those of the third test (battlefield 3) are shown in Tables 7 and 8. Table 4, Table 6, and Table 8 present the statistical results obtained from the [PERSON]'s rank-sum nonparametric statistical test, which was performed with a significance level of 0.05. The best results and rankings are shown in bold.