## 3 Methodology
### Feature Optimization  
After feature extraction, the IDOA is proposed for discriminative feature selection, where IDOA is a metaheuristic optimization algorithm that mimics the dynamic and static behaviors of dragonflies. The feature selection process is considered a problem of global combinatorial optimization, which seeks to reduce the quantity of features and redundant, noisy, and redundant data, while producing a uniform level of classification accuracy. As a result, this research introduces the Improved Dragonfly Optimization Technique (IDOA), an optimization algorithm that performs better. The discrete search space consists of all feasible arrangements of attributes chosen from the dataset. It might be possible to list every potential subset of characteristics, given the limited number of features. The improved dragonfly employs more group knowledge to influence its own behavior, ensuring that the group is diverse, and creating a balance between the stages of exploration and exploitation to increase the algorithm's search efficiency. This feature selection technique is typically quicker and more efficient, and minimizes overfitting, which eliminates redundant and noisy data to find a subset of relevant features using the strength of the IDOA to improve classification outcomes. The two major phases in the IDOA are exploitation and exploration, which are modeled statically or dynamically, either by avoiding the enemy or searching for food [40]. Usually, the swarms have three behaviors: cohesion, alignment, and separation. Furthermore, two additional behaviors are added to these three behaviors in the IDOA: avoiding the enemy and moving towards food. The purpose of including these two behaviors in the IDOA is to increase the survival time of the swarm. In this algorithm, two vectors are considered: the position and step for updating the position of dragonflies in a search space. The step vector is also considered as the speed that determines the direction of dragonflies. After the step vector calculation, the position vector is updated.  
In the IDOA, the coefficients (cohesion, alignment, separation, food factor, inertia coefficient, enemy factor, and iteration number) are enabled to perform exploitative and exploratory behaviors. The cohesion coefficient is high and the alignment coefficient is low in the exploitation process; conversely, the cohesion coefficient is low and the alignment coefficient is high in the exploration process. In the conventional DOA, the Levy flight mechanism is used to enhance the probabilistic behavior, randomness, and the discovery of artificial dragonflies. Hence, the Levy flight mechanism improves the DOA efficacy to a certain extent. However, the step size control is contrary to the nature of the Levy flight mechanism. The agents have to move outside the search space, if a long step is considered. To overcome these issues, the Brownian motion (\(P_{g}\)), is considered in the IDOA for enhancing the probabilistic behavior, randomness, and the discovery of dragonflies. The Brownian motion (\(P_{g}\)) is mathematically determined in Equations (17) and (18):  
\[P_{g}=\frac{1}{s\sqrt{2\pi}}exp\left(-\frac{(dimension-agents)^{2}}{2s^{2}}\right) \tag{17}\]  
\[s=\sqrt{\frac{m_{t}}{m_{s}}}\text{, and }m_{s}=100\times m_{t} \tag{18}\]  
where \(m_{t}=0.01\) indicates the motion time of an agent, and \(m_{s}\) specifies the number of sudden motions. The parameter settings of the IDOA are as follows: the number of search agents is five, the search domain is [0-1], the dimension is equal to the extracted feature vectors, and the number of iteration is 20. The proposed IDOA selects 3476 feature vectors, which are used as input values in the DBN for classification.