## Appendix A A Comprehensive Example of PDFH, FSDFH, and SSDFH Selection Utilizing the MPA
\begin{array}{c}\text{\emph{Fitness}}_{old}=\begin{array}{c}\text{CDFH}_{1 }\\ \text{CDFH}_{2}\\ \text{CDFH}_{3}\\ \text{CDFH}_{4}\\ \text{CDFH}_{5}\\ \text{CDFH}_{6}\end{array}\begin{bmatrix}0.1244\\ 0.2326\\ 0.6449\\  
typically only needed to solve the integral in Equation (3) when \(x\) has a huge value, as in the following cases:  
\[f_{L}\approx\frac{\gamma\Gamma(1+\beta)sin\Big{(}\frac{\pi\beta}{2}\Big{)}}{\pi x ^{(1+\beta)}},\ \ \ x\rightarrow\infty, \tag{10}\]  
where the Gamma function is symbolized by \(\Gamma\), and \(\Gamma(1+\beta)=\beta!\), for integer \(\beta\) values. For every index distribution (\(\beta\)) value between 0.3 and 1.99, [PERSON] presented a quick and precise approach for creating a Levy steady procedure. In this study, random numbers based on the Levy distribution are generated using the Mantegna method as follows [60]:  
\[Levy(\beta)=0.05\times\frac{x}{|y|^{(1/\beta)}}. \tag{11}\]  
Assuming two variables, \(u\) and \(y\), have normal distributions and have the following standard deviations:  
\[x=Normal\ (0,\sigma_{x}^{2}), \tag{12}\]  
\[y=Normal\ (0,\sigma_{y}^{2}), \tag{13}\]  
where \(\sigma_{y}^{2}=1\), and \(\sigma_{x}^{2}\) is computed as follows [60]:  
\[\sigma_{x}=\left(\frac{\Gamma(1+\beta)\times sin(\frac{\pi\beta}{2})}{\Gamma( \frac{1+\beta}{2})\times\beta\times 2^{(\frac{\beta-1}{2})}}\right)^{(\frac{1}{ \beta})}, \tag{14}\]  
Table 14 summarizes the parameters used to find the value of \(\sigma_{x}\).  
Numerator of \(\sigma_{x}=\Gamma(1+\beta)\times sin(\frac{\pi\beta}{2})=\beta!\times sin( \frac{\pi\beta}{2})=(1.5)!\times sin(\frac{\pi(1.5)}{2})=1.329\times 0.707=0.9400\). (15)  
Denominator of \(\sigma_{x}=\Gamma(\frac{1+\beta}{2})\times\beta\times 2^{(\frac{\beta-1}{2})}= \Gamma(\frac{1+1.5}{2})\times 1.5\times 2^{(\frac{15-1}{2})}=0.926\times 1.5 \times 1.189=1.6169\). (16)  
\[\sigma_{x}=\left(\frac{0.9400}{1.6169}\right)^{(\frac{1}{\beta})}=\left(\frac {0.9400}{1.6169}\right)^{(\frac{1}{15})}=0.6966.\]  
where \(x\) and \(y\) are two random variables and are generated based on the normal distribution as follows:  
\[x=\text{random }(\text{'Normal}^{\prime},\ \mu_{x},\sigma_{x},n,m)=\text{ random }(\text{'Normal}^{\prime},1,0.9666,6,5). \tag{17}\]\[y=\text{random}\left({{}^{\text{Normal}}}^{\prime},\,\mu_{y},\sigma_{y},n,m\right)= \text{random}\left({{}^{\text{-Normal}}}^{\prime},0,1,6,5\right). \tag{11}\]  
The output \(Z\) represents a Levy random number vector and is evaluated in the following equation:  
\[\overset{\rightarrow}{Z}=\overset{\rightarrow}{x}/(\left|\overset{ \rightarrow}{y}\right|^{(1/\beta)})=(1.6441)/\left({-\,0.6222}\right)^{(1/1.5)} =2.2559. \tag{12}\]  
\[\overset{\rightarrow}{x}=\begin{bmatrix}1.6441&-0.5986&-1.1043&-0.0065&-1.238 0\\ -0.1677&0.6727&-0.4725&-0.4006&-0.2009\\ -0.0919&-0.3219&-0.6651&-0.4711&-0.3163\\ -0.3865&0.2284&0.4246&-0.7888&0.5500\\ -0.3475&-0.3868&0.1547&-0.7319&0.0273\\ 0.3800&-0.2606&-1.1319&0.0820&-1.2275\end{bmatrix},\overset{\rightarrow}{y}= \begin{bmatrix}-0.6222&0.3884&-0.6646&0.5094&1.1456\\ -0.8115&-0.0943&0.6936&0.0367&-1.3455\\ 0.8456&-0.9878&1.2105&0.5184&-1.4030\\ -0.1955&0.6283&0.4038&0.3969&0.7398\\ -0.1133&-0.8988&-0.7261&0.0867&0.2544\\ -0.0539&0.3592&0.2589&0.9676&0.3213\end{bmatrix}\]  
\[\overset{\rightarrow}{Z}=\begin{bmatrix}2.2559&-1.1246&-1.4501&-0.0102&-1.1 307\\ -0.1928&3.2479&-0.6030&-3.6274&-0.1649\\ -0.1028&-0.3246&-0.5856&-0.7300&-0.2524\\ -1.1473&0.3114&0.7772&-1.4604&0.6724\\ -1.4843&-0.4154&0.1915&-3.7352&0.0679\\ 2.6636&-0.5157&-2.7865&0.0838&-2.6168\end{bmatrix}\]  
The random numbers generated by the Levy distribution are represented in a vector \(RL\) based on the following equation:  
\[\overrightarrow{RL}=0.05\times\overset{\rightarrow}{Z}=0.05\times 2.2559=0.1128 \tag{13}\]  
\(RB\) is a vector containing random numbers generated by the standard Brownian motion. The standard Brownian motion is defined as a stochastic process with step length that is obtained from the probability function characterized by Normal (Gaussian) distribution with unit variance (\(\sigma_{2}=1\)) and a zero mean (\(\mu=0\)) Brownian random number vector.  
\[\overrightarrow{RL}=\begin{bmatrix}0.1128&-0.0562&-0.0725&-0.0005&-0.0565\\ -0.0096&0.1624&-0.0301&-0.1814&-0.0082\\ -0.0051&-0.0162&-0.0293&-0.0365&-0.0126\\ -0.0574&0.0156&0.0389&-0.0730&0.0336\\ -0.0742&-0.0208&0.0096&-0.1868&0.0034\\ 0.1332&-0.0258&-0.1393&0.0042&-0.1308\end{bmatrix},\overrightarrow{RB}= \begin{bmatrix}-0.0630&1.0786&0.6528&2.1233&0.6213\\ -0.6377&0.4490&-2.1236&0.0488&0.2436\\ 1.3301&-1.1289&-0.6994&0.5156&-0.2499\\ 0.0419&0.4730&0.0136&0.2060&-0.8973\\ 1.0329&0.2812&0.8853&-1.1249&0.8534\\ 0.6014&-1.6456&0.7952&-0.6864&-1.0708\end{bmatrix}\]  
Step 5: Constructing \(\overrightarrow{StepSize}\) and updating \(\overrightarrow{Prey}\):  
In this step, each element in \(\overrightarrow{Prey}\) must be updated to an \(i\) row number and \(j\) column number, as shown in Table 18:  
\[StepSize(i,j)=RB(i,j)\times(Elite(i,j)-RB(i,j)\times Prey(i,j)=-0.0630\times(0 -(-0.0630)\times 0.1875)=-0.0007 \tag{14}\]  
\[Prey(i,j)=Prey(i,j)+P\times R\times stepsize(i,j)=0.1875+0.5\times 0.6581\times-0.0007 =0.1872 \tag{15}\]Accordingly, \(\overrightarrow{StepSize}\) is represented as below:  
\[\overrightarrow{StepSize}=\begin{bmatrix}-0.0007&-0.0242&-0.0378&0&0.2805\\ -0.0030&-0.0001&-4.5099&0&01408\\ -1.7692&-1.2744&-0.0814&0&-0.1937\\ -0.0009&-0.0273&-0.0000&0&-1.1917\\ -0.3160&-0.0030&-0.2249&0&0.5246\\ 0&0&0&0&-1.5523\\ \end{bmatrix}\]  
The following is the new matrix of _Prey_ after updating its values based on Equations (A18) and (A19).  
\[\overrightarrow{Prey}=\begin{bmatrix}D_{CDFH_{l}\to Sink}&Inv\big{(}RE_{CDFH_{l} }\big{)}&Avg\big{(}D_{CDFH_{l}\to DFGMs}\big{)}&X_{CDFH_{l}}&ASBO_{CDFH_{l}}\\ \text{CDFH}_{1}&\begin{bmatrix}0.1875&0.0208&0.0886&0&0.4\\ 0.0073&0.0006&1.0000&0.5\\ \text{CDFH}_{3}&1.0000&1.0000&0.1663&0&0.3\\ \text{CDFH}_{4}&0.4893&0.1219&0.0866&0&0.7\\ \text{CDFH}_{5}&0.2962&0.0375&0.2870&0&0.1\\ \text{CDFH}_{6}&0&0&0&0&0.7\\ \end{bmatrix}\]  
\[\overrightarrow{Prey}=\begin{bmatrix}D_{CDFH_{l}\to Sink}&Inv\big{(}RE_{CDFH_{l} \to DFGMs}\big{)}&Avg\big{(}D_{CDFH_{l}\to DFGMs}\big{)}&X_{CDFH_{l}}&ASBO_{CDFH_{l}}\\ \text{CDFH}_{1}&\begin{bmatrix}0.1875&0.0208&0.0886&0&0.4\\ 0.0  
\begin{tabular}{c c c c c c c} \hline \hline \((i,j)\) & \(R\) & \(RB\) & \(\mathbf{\text{\em Elite (}i,j)}\) & \(\mathbf{\text{\em Prey (}i,j)}\) & \(\mathbf{\text{\em Before Update}}\) & \(\mathbf{\text{\em Step-Size (}i,j)}\) & \(\mathbf{\text{\em Prey (}i,j)}\) & \(\mathbf{\text{\em After Update}}\) \\ \hline \((5,1)\) & \(0.9855\) & \(1.0329\) & \(0\) & \(0.2962\) & \(-0.3160\) & \(0.1405\) \\ \hline \((5,2)\) & \(0.4213\) & \(0.2812\) & \(0\) & \(0.0375\) & \(-0.0030\) & \(0.0369\) \\ \hline \((5,3)\) & \(0.2375\) & \(0.8853\) & \(0\) & \(0.2870\) & \(-0.2249\) & \(0.2603\) \\ \hline \((5,4)\) & \(0.3912\) & \(-1.1249\) & \(0\) & \(0\) & \(0\) & \(0\) \\ \hline \((5,5)\) & \(0.4965\) & \(0.8534\) & \(0.7000\) & \(0.1000\) & \(0.5246\) & \(0.2302\) \\ \hline \((6,1)\) & \(0.1290\) & \(0.6014\) & \(0\) & \(0\) & \(0\) & \(0\) \\ \hline \((6,2)\) & \(0.2768\) & \(-1.6456\) & \(0\) & \(0\) & \(0\) & \(0\) \\ \hline \((6,3)\) & \(0.9624\) & \(0.7952\) & \(0\) & \(0\) & \(0\) & \(0\) \\ \hline \((6,4)\) & \(0.2585\) & \(-0.6864\) & \(0\) & \(0\) & \(0\) & \(0\) \\ \hline \((6,5)\) & \(0.3891\) & \(-1.0708\) & \(0.7000\) & \(0.7000\) & \(-1.5523\) & \(0.3980\) \\ \hline \hline \end{tabular}  
Step 6: Detecting top predator  
After updating \(\overrightarrow{\text{\em Prey}}\), the new fitness is calculated as shown in Table 10 and compared with the top predator fitness. Once SA's fitness is less than the top predator fitness, the top predator fitness is updated to be equal to this fitness.  
\begin{tabular}{c c c} \hline \hline \(\text{\em CDFH}_{1}\) & \(\left[\begin{matrix}0.1260\\ 0.2267\\ 0.2267\\ 0.5082\\ 0.0398\end{matrix}\right]\) & \(\left[\begin{matrix}0.1260\\ 0.2267\\ 0.5082\\ 0.2559\\ 0.0398\end{matrix}\right]\) \\ \hline \hline \end{tabular}  
Step 7: Memory saving  
Since we are still in \(\mathit{Iter}=0\), the new fitness is saved in \(\overrightarrow{\text{\em Fitness}_{old}}\), and the new \(\overrightarrow{\text{\em Prey}}\) is saved in \(\overrightarrow{\text{\em Prey}_{old}}\).  
\begin{tabular}{c c c c c c c c} \hline \hline \(\text{\em CDFH}_{1}\) & \(\left[\begin{matrix}0.1260\\ 0.2267\\ 0.5082\\ 0.2559\\ 0.1264\\ 0.0398\end{matrix}\right]\) \\ \hline \hline \end{tabular}  
Step 8: The new fitness is saved in \(\overrightarrow{\text{\em Prey}_{old}}\).  
Step 9: The new fitness is saved in \(\overrightarrow{\text{\em Prey}_{old}}\).  
Step 10: The new fitness is saved in \(\overrightarrow{\text{\em Prey}_  
\begin{tabular}{c c c c c c}  & \(D_{\mathit{CDFH_{i}\to Sink}}\) & \(Inv(RE_{\mathit{CDFH_{i}}})\) & \(Avg\big{(}D_{\mathit{CDFH_{i}\to DFGMs}}\big{)}\) & \(X_{\mathit{CDFH_{i}}}\) & \(ASBO_{\mathit{CDFH_{i}}}\) \\ \(\mathit{\overrightarrow{\mathit{Prey\_old}}}=\) & \(\mathit{\overrightarrow{\mathit{Prey\_old}}}\) & \(\mathit{\overrightarrow{\mathit{Prey\_old}}}\) & \(\mathit{\overrightarrow{\mathit{[PERSON]\_old}}}\) & \(\mathit{\overrightarrow{\mathit{[PERSON]\_old}}}\) \\  & \(\mathit{\overrightarrow{\mathit{[PERSON]\_old}}}\) & \(\mathit{\overrightarrow{\mathit{[PERSON]\_old}}}\) & \(\mathit{\overrightarrow{\mathit{[PERSON]\_old}}}\) & \(\mathit{\overrightarrow{\mathit{[PERSON]\_old}}}\) & \(\mathit{\overrightarrow{\mathit{[PERSON]\_old}}}\) \\  & \(\mathit{\overrightarrow{\mathit{[PERSON]\_old}}}\) & \(\mathit{\overrightarrow{\mathit{[PERSON]\_old}}}\) & \(\mathit{\overrightarrow{\mathit{[PERSON]\_old}}}\) & \(\mathit{\overrightarrow{\mathit{[PERSON]\_old}}}\) & \(\mathit{\overrightarrow{\mathit{[PERSON]\_old}}}\) \\  & \(\mathit{\overrightarrow{\mathit{[PERSON]\_old}}}\) & \(\mathit{\overrightarrow{\mathit{[PERSON]\_old}}}\) & \(\mathit{\overrightarrow{\mathit{[PERSON]\_old}}}\) & \(\mathit{\overrightarrow{\mathit{[PERSON]\_old}}}\) \\  & \(\mathit{\overrightarrow{\mathit{[PERSON]\_old}}}\) & \(\mathit{\overrightarrow{\mathit{[PERSON]\_old}}}\) & \(\mathit{\overrightarrow{\mathit{[PERSON]\_old}}}\) & \(\mathit{\overrightarrow{\mathit{Prey\_old}}}\) \\  & \(\mathit{\overrightarrow{\mathit{Prey\_old}}}\) & \(\mathit{\overrightarrow{\mathit{Prey\_old}}}\) & \(\mathit{\overrightarrow{\mathit{Prey\_old}}}\) & \(\mathit{\overrightarrow{\mathit{Prey\_old}}}\) \\ \end{tabular}  
Now, if \(\overrightarrow{\mathit{Prey\_old}}\) < \(\overrightarrow{\mathit{Prey\_old}}\), set the return value to \(\overrightarrow{\mathit{Prey\_old}}\), and repeat these values to form \(\overrightarrow{\mathit{Prey\_old}}\).  
\begin{tabular}{c c c c}  & \(\mathit{\overrightarrow{\mathit{Prey\_old}}}\) & \(\mathit{\overrightarrow{\mathit{Prey\_old}}}\) & \(\mathit{\overrightarrow{\mathit{Prey\_old}}}\) \\  & \(\mathit{\overrightarrow{\mathit{Prey\_old}}}\) & \(\mathit{\overrightarrow{\mathit{Prey\_old}}}\) & \(\mathit{\overrightarrow{\mathit{Prey\_old}}}\) \\  & \(\mathit{\overrightarrow{\mathit{Prey\_old}}}\) & \(\mathit{\overrightarrow{\mathit{Prey\_old}}}\) & \(\mathit{\overrightarrow{\mathit{Prey\_old}}}\) \\  & \(\mathit{\overrightarrow{\mathit{Prey\_old}}}\) & \(\mathit{\overrightarrow{\mathit{Prey\_old}}}\) & \(\mathit{\overrightarrow{\mathit{Prey\_old}}}\) \\  & \(\mathit{\overrightarrow{\mathit{Prey\_old}}}\) & \(\mathit{\overrightarrow{\mathit{Prey\_old}}}\) & \(\mathit{\overrightarrow{\mathit{Prey\_old}}}\) \\  & \(\mathit{\overrightarrow{\mathit{Prey\_old}}}\) & \(\mathit{\overrightarrow{\mathit{Prey\_old}}}\) & \(\mathit{\overrightarrow{\mathit{Prey\_old}}}\) \\ \end{tabular}  
It is noteworthy that these matrices are used to update \(\overrightarrow{\mathit{Prey\_old}}\) and \(\overrightarrow{\mathit{Prey\_old}}\) if the old values are better than the current values based on Equations (A2) and (A3) (i.e., memory saving). Since there is no memory of the previous values, \(\overrightarrow{\mathit{Prey\_old}}\) and \(\overrightarrow{\mathit{Prey\_old}}\) remain the same without any change; the next step is to assign these matrices to \(\overrightarrow{\mathit{Prey\_old}}\) and \(\overrightarrow{\mathit{Prey\_old}}\). Thus, the values of these four matrices stay unchanged, without alterations.  
Step 8: Eddy formation and FADs' effect  
Generate a random number--in our example \(r=0.8193\)--then compare it with FADs = 0.2 (i.e., 0.8193 < 0.2); if the condition is false, then set \(Rs=6\), which is the number of SAs, then calculate the step size and update \(\overrightarrow{\mathit{Prey\_old}}\) based on the following equation:  
\[\mathit{StepSize}=(\mathit{FADs}\times(1-r)+r)\times(\mathit{Prey}(rand(Rs),: )-\mathit{Prey}(rand(Rs),:)).\] (A20)  
\[\mathit{Prey}=\mathit{Prey}+\mathit{StepSize}.\] (A21)  
\(\overrightarrow{\mathit{StepSize}}\) is represented as follows:  
\[\overrightarrow{\mathit{StepSize}}=\begin{bmatrix}-0.4185&-0.0966&-0.0740&0&-0. 0639\\ 0.4185&0.0966&0.0740&0&0.0639\\ -0.1549&-0.0077&0.7393&0&0.0720\\ -0.6796&-0.4509&0.1009&0&-0.0379\\ 0.7945&0.4819&-0.6871&0&-0.2293\\ 0.0400&-0.0234&-0.1531&0&0.1952\\ \end{bmatrix}\]  
As a result, \(\overrightarrow{\mathit{Prey\_old}}\) is constructed as below:  
\[\stackrel{{\rightarrow}}{{Inx}}=\begin{bmatrix}\text{CDFH}_{1}&0\\ \text{CDFH}_{2}&0\\ \text{CDFH}_{3}&0\\ \text{CDFH}_{4}&0\\ \text{CDFH}_{5}&0\\ \text{CDFH}_{6}&0\end{bmatrix},\quad\stackrel{{\rightarrow}}{{Indx }}=\begin{bmatrix}\text{CDFH}_{1}&0&0&0&0\\ 0&0&0&0&0\\ 0&0&0&0&0\\ 0&0&0&0&0\\ 0&0&0&0&0\\ 0&0&0&0&0\\ 0&0&0&0&0\\ 0&0&0&0&0\end{bmatrix}\]  
It is noteworthy that these matrices are used to update \(\stackrel{{\rightarrow}}{{Prey}}\) and \(\stackrel{{\rightarrow}}{{Fitness}}\) if the old values are better than the current values based on Equations (A2) and (A3) (i.e., memory saving). The \(\stackrel{{\rightarrow}}{{Prey}}\) and \(\stackrel{{\rightarrow}}{{Fitness}}\) are shown below; the next step is to assign these matrices to \(\stackrel{{\rightarrow}}{{Prey}}_{old}\) and \(\stackrel{{\rightarrow}}{{Fitness}}_{old}\).  
\[\stackrel{{\rightarrow}}{{Prey}}_{old}=\begin{bmatrix}\text{CDFH}_{1} &0.0408\\ \text{CDFH}_{2}&0.2267\\ \text{CDFH}_{3}&0.5082\\ \text{CDFH}_{4}&0.0772\\ \text{CDFH}_{5}&0.1264\\ \text{CDFH}_{6}&0.0398\  
\[\overrightarrow{Z}=\begin{bmatrix}0.7166&1.8661&-0.7016&0.2178&0.2129\\ 0.3016&2.6027&-0.8017&-3.5309&0.3018\\ -1.6174&-0.1585&0.9696&-0.6130&-5.6605\\ -0.8855&0.0653&0.3509&-0.2208&4.6833\\ -0.7752&-1.3967&-0.0610&0.0342&-0.8094\\ 0.8319&-0.2247&-0.6640&-0.7646&-0.4254\end{bmatrix}\]  
The random numbers generated by the Levy distribution and the standard Brownian motion are represented in a vector RL and RB, respectively, as shown in the previous iteration:  
\[\overrightarrow{RL}=\begin{bmatrix}0.0358&0.0933&-0.0351&0.0109&0.0106\\ 0.0151&0.1301&-0.0401&-0.1765&0.0151\\ -0.0809&-0.0079&0.0485&-0.0306&-0.2830\\ -0.0443&0.0033&0.0175&-0.0110&0.2342\\ -0.0388&-0.0698&-0.0031&0.0017&-0.0405\\ 0.0416&-0.0112&-0.0332&-0.0382&-0.0213\end{bmatrix},\overrightarrow{RB}= \begin{bmatrix}-1.1131&0.1674&1.1836&-1.8613&-0.7175\\ 0.1998&-0.3666&0.0636&-0.9790&-3.4346\\ 0.0450&2.1763&0.7288&-2.2373&0.5227\\ -1.3118&0.7794&0.2594&1.4843&-0.0751\\ -03726&-1.1371&-0.3020&-0.4157&0.4437\\ 0.5113&-1.4291&0.9560&-0.3061&-0.7265\end{bmatrix}\]  
Step 5: Constructing \(\overrightarrow{Stepsize}\) and updating \(\overrightarrow{Prey}\):  
In this step, each element in \(\overrightarrow{Prey}\) must be updated to an \(i\) row number and \(j\) column number based on Equations (A18) and (A19), as shown in Table A8. Accordingly, \(\overrightarrow{Stepsize}\) is represented as below:  
\[\overrightarrow{StepSize}=\begin{bmatrix}0&0&-0.0102&0&-0.4887\\ -0.0002&-0.0001&-0.0038&0&-7.7682\\ -0.0019&-2.6709&-0.0756&0&0.1330\\ 0&0&-0.0126&0&-0.0323\\ -0.0195&-0.0477&-0.0237&0&0.1313\\ 0&0&0&0&-0.4992\end{bmatrix}\]  
The following is the new matrix of \(\overrightarrow{Prey}\) after updating its values based on Equations (A18) and (A19).  
\[\overrightarrow{Prey}=\begin{bmatrix}D_{CDFH_{i}\to Sink}&Inv\big{(}RE_{CDFH_{i} }\big{)}&Avg\big{(}D_{CDFH_{i}\to DFGMs}\big{)}&X_{CDFH_{i}}&ASBO_{CDFH_{i}}\\ \begin{array}{llll}\text{CDFH}_{1}&0&0&0.0052&0&0.3643\\ \text{CDFH}_{2}&0.0061&0.0006&0.945  
After updating \(\overrightarrow{\textit{Prey}}\), the new fitness is calculated and compared with the top predator fitness. Once SA's fitness is less than the top predator fitness, the top predator fitness is updated to be equal to this fitness, as presented in Table 10.  
\[\overrightarrow{\textit{Fitness}}=\begin{array}{l}\text{CDFH}_{1}\\ \text{CDFH}_{2}\\ \text{CDFH}_{3}\\ \text{CDFH}_{4}\\ \text{CDFH}_{5}\\ \text{CDFH}_{6}\end{array}\begin{bmatrix}0.0374\\ 0.1724\\ 0.3642\\ 0.0759\\ 0.1242\\ 0.0167\end{bmatrix}\]\[\overrightarrow{Inv}=\begin{array}{c}\begin{bmatrix}\text{CDF}{{\color[rgb]{0,0,0} \definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0} \pgfsys@color@gray@fill{0}{0}\pgfsys@setlinewidth{0.4 pt}{0.4 pt}}}\\ \text{CDF}{{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0} \pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}{0}\pgfsys@setlinewidth{0.4 pt}{0.4 pt}}} \\ \text{CDF}{{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0} \pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}{0}\pgfsys@setlinewidth{0.4 pt}{0.4 pt}} }\\ \text{CDF}{{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0} \pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}{0}\pgfsys@setlinewidth{0.4 pt}{0.4 pt}} }\\ \text{CDF}{{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0} \pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}{0}\pgfsys@setlinewidth{0.4 pt}{0.4 pt}} }\\ \text{CDF}{{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0} \pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}{0}\pgfsys@setlinewidth{0.4 pt}{0.4 pt}} }\\ \text{CDF}{{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0} \pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}{0}\pgfsys@setlinewidth{0.4 pt}{0.4 pt}} }\\ \text{CDF}{{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0} \pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}{0}\pgfsys@setlinewidth{0.4 pt}{0.4 pt}} }\\ \text{CDF}{{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0} \pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}{0}\pgfsys@setlinewidth{0.4 pt}{0.4 pt}} }\\ \end{array}\end{array}\end{array}\begin{bmatrix}0\\ 0\\ 0\\ 0\\ 0\\ 0\\ 0\\ 0\\ 0\\ 0\\ 0\\ 0\\ 0\\ 0\\ 0\\ 0\\ 0\\ 0\\ 0\\ \end{bmatrix}