## 3 The Proposed Model
### Feature Selection Using HEGSO Algorithm  
To select features accurately, the HEGSO algorithm is exploited in this study. The classical GSO technique is based on glowworm behaviors and was rooted in the natural activity of glowworms during the night [23]. The glowworm exhibits a kind of communication with other glowworms in a group depending on the luciferin. If the luciferin was high, then the light produced by the glowworm was high. As a result, the glowworm goes towards it. In this neighborhood, Luciferin update, movement, and update phases are the three different stages of the GSO technique. The luciferin update stage refers to luciferin creation. The quantity of luciferin was directly proportional to the fitness of the current site on the main function. During the movement stage, the glowworm selects the use of probabilistic means to go towards the neighbor where the value is high. The succeeding stage involves the adaptive neighborhood  
Figure 2: Overall flow of XAIID-SCPS system. range for perceiving the peak presence. The optimizing process can be performed by using the HEGSO technique. The presented study applies the hybrid model where the HEGSO technique is used. Moreover, the usage of two genetic algorithms includes mutation and crossover operators. The optimization algorithm is initiated when the path is found between the beginning and the end points of the vehicular nodes that are presented in the road segment. The HEGSO method can be given in the following,  
Step 1: Initialize the 'Eearch Agent' (EA), namely, separate glowworm \(X\) and the population size can be represented as \(Q\). \(z\) shows the step size, the maximum number of iterations is represented by \(N_{it}\), \(l_{o}\) indicates the initial value of luciferin, \(r_{o}\) indicates the primary value of radial gamut of SA, and \(n\) denotes the hour's instance. \[X=\left\{x_{1},\ x_{2},\ \ldots\ x_{Q}\right\} \tag{2}\]  
Step 2: Calculate the fitness function using the following expression:  
\[F=\sum_{r=1}^{n}\frac{L}{EV_{t}(V_{i})} \tag{3}\]  
where \(L\) denotes the overall length of the road segment, the average velocity of the vehicle on road was represented as \(EV_{t}\) and \(V_{i}\) specify the vehicle. Step 3: In the luciferin stage, every new SA is calculated by the following equation:  
\[l_{j}(n+1)=(1-\rho)l_{j}(n)+\beta l_{j}(n+1) \tag{4}\]  
where \(l(n)\) indicates the luciferin value of SA at \(n\) time, \(l_{j}(n+1)\) indicates the luciferin value of SA at \((n+1)\) time, \(J_{j}\) denotes fitness function, and \(\beta\) denotes luciferin decay constant with the value in gamut [0, 1]. Step 4: The main function of every novel SA is evaluated by a similar fitness function that was declared in step 2. Step 5: Now, the SA headed for the neighboring glowworm was based on luminance. The SA movement can be determined by Euclidean distance among the glowworms. Here, SA heads for evaluated neighborhoods based on the probabilistic model, which can be expressed as follows. \[x_{j}(n+1)=x_{j}(n)+Z\Bigg{(}\frac{xk(n)-x_{j}(n)}{\left\|xk(n)-x_{j}(n) \right\|}\Bigg{)} \tag{5}\]  
In Equation (5), \(Z\) denotes step size. Step 6: Here, the decision range was upgraded, namely, the neighborhood range of SA is upgraded as follows:  
\[r_{d}(n+1)=\ \min\ (r_{s},\ \max\ (O,\ r_{d}(n))+B\big{(}n_{e}-\left|N_{j}(n) \right|\big{)} \tag{6}\]  
In Equation (6), \(B\) indicates the constant, \(r_{s}\) represent the largest sensing radius of the glowworm. \(n_{e}\) shows the SA having a higher luciferin value in the decision range, \(r_{d}(n+1)\) and \(rd\) denote the upgraded and preceding values of the neighborhood range. Step 7: Check whether the ending condition is met. When the ending condition is satisfied, then a better solution can be attained. When the ending conditions are not satisfied after two genetic operators, namely, crossover and mutation operators, are used. Furthermore, the 2-point crossover was used. This can be followed by a mutation process where there are specific genes.