## 6 Experimental Results
### Experimental Results
The experimental results of the first test instance show that the QWDO algorithm has fast convergence rate, high convergence precision, and it is an effective and feasible solution in solving the UCAV path-planning problem. Figure 10: Fitness of the first instance for \(D=15\). Figure 9: Fitness of the first instance for \(D=10\). In order to verify the performance of the QWDO algorithm for solving the problem of UCAV path planning more fully, in this section, all the algorithms are applied to the second test instance. Similarly, the dimension \(D\) and the maximum number of iterations _Maxgen_ are used as the two control variables. We look at the performance of QWDO algorithm as compared with other optimization algorithms, such as, quantum bat algorithm (QBA), quantum particle swarm optimization (QPSO), wind driven optimization (WDO), bat algorithm (BA), particle swarm optimization (PSO), and differential evolution (DE). In the second test instance, UCAV starts at (10,15) and finishes at (80,75). In this battlefield environment, there are eight threat centers. Table 11 shows information about known threats for the second test instance. Figure 11: Fitness of the first instance for \(D=20\). Figure 12: Fitness of the first instance for \(D=25\). \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline _Popsize_ & _Maxgen_ & \(D\) & **Result** & **DE** & **PSO** & **BA** & **WDO** & **QPSO** & **QBA** & **QWDO** \\ \hline \multirow{3}{*}{30} & \multirow{3}{*}{50} & \multirow{3}{*}{20} & Mean & 51.55066072 & 57.49611763 & 55.52925757 & 53.37244656 & 55.06247744 & 53.26816353 & **51.0384824** \\  & & & Best & 50.86230831 & 52.4021891 & 50.8335776 & 51.63208235 & 52.8750534 & 51.2628276 & **50.56417883** \\  & & & Worst & **52.97970342** & 67.05488964 & 63.29522516 & 54.20131962 & 61.773734 & 58.6281585 & 53.31025066 \\ \hline \multirow{3}{*}{30} & \multirow{3}{*}{100} & \multirow{3}{*}{20} & Mean & 51.517437395 & 52.4422386 & 53.18924498 & 53.4000727 & 52.13986363 & 51.5334754 & **51.0411419** \\  & & & Best & 50.9839101 & 50.68887829 & 51.00543513 & 51.88086349 & 51.22949906 & 50.67123961 & **50.39932388** \\  & & & Worst & **52.51750608** & 59.61227827 & 58.92518468 & 54.23101102 & 54.2993175 & 55.38455421 & 52.93418088 \\ \hline \multirow{3}{*}{30} & \multirow{3}{*}{150} & \multirow{3}{*}{20} & Mean & 51.93773208 & 50.91881032 & 53.00666682 & 53.14375161 & 51.37913337 & 51.48087993 & **50.76428055** \\  & & & Best & 50.76460097 & 50.41768377 & 50.6384468 & 51.90565475 & 50.63851986 & 50.61691071 & **50.39664491** \\  & & & Worst & 59.1205155 & 53.27343125 & 61.22625354 & 54.01364469 & 55.1714668 & 54.98546674 & **52.91230338** \\ \hline \multirow{3}{*}{30} & \multirow{3}{*}{200} & \multirow{3}{*}{20} & Mean & 50.73461503 & 51.67395413 & 53.26005394 & 53.15550115 & 51.39219052 & 51.02583378 & **50.58751325** \\  & & & Best & 50.4160651 & 50.91067104 & 50.62680554 & 52.15259159 & 50.51985597 & 50.5578439 & **50.39570371** \\  & & & Worst & 53.3008547 & 54.17616743 & 61.10842324 & 54.1708213 & 53.93215589 & 53.50379863 & **52.86341706** \\ \hline \multirow{3}{*}{30} & \multirow{3}{*}{250} & \multirow{3}{*}{20} & Mean & 51.46928513 & **50.7031705** & 52.63088516 & 52.899882545 & 50.888980761 & 51.34376512 & 50.91479454 \\  & & & Best & 50.786484292 & 50.42161575 & 50.5392192 & 51.96380792 & 50.49238732 & 50.49715669 & **50.39447178** \\ \cline{1-1}  & & & Worst & 54.08790265 & 53.06705989 & 60.68415725 & 53.67059243 & 53.42973728 & 57.02850459 & **52.8744808** \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Experimental results for the first test instance in different _Maxgen_. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Threat Center**} & **(59,52)** & **(55,80)** & **(27,58)** & **(24,33)** & **(12,48)** & **(70,65)** & **(70,34)** & **(30,70)** \\ \hline \multicolumn{2}{c}{Threat radius} & 10 & 9 & 9 & 9 & 12 & 7 & 12 & 10 \\ \multicolumn{2}{c}{Threat level} & 9 & 7 & 3 & 12 & 1 & 5 & 13 & 2 \\ \hline \hline \end{tabular}
\end{table}