# An Improved Machine Learning Model with Hybrid Technique in VANET for Robust Communication
## 5 Simulated Steps for Proposed Methodology
### Phase: 2--Reliable Resource Management Using H-WDFOA-VANET  
This paper uses hybrid whale-dragonfly optimization-based VANET (H-WDFOA-VANET) for reliable resource management to allocate bandwidth. The proposed H-WDFOA approach is explained, then the background of WOA and DF optimization techniques are individually discussed. In a dynamic VANET scenario, a combination of whale and dragonfly optimization algorithms is represented as H-WDFOA. To achieve bandwidth allocation and obtain routing of the vehicle, the H-WDFOA was employed. Protocol routing also initiated latency and bandwidth utilization to get the designation path. It was difficult during a safety-related and critical time. As a result, to solve this issue, the efficient H-WDFOA technique was utilized to allocate bandwidth and routing protocol. In a dynamic VANET environment, to see the effect of QoS value, meta-heuristic-based WOA and DF techniques are coupled in this proposed methodology. The hunting behaviour of humpback whales was considered for the whale optimization algorithm (WOA) generation. The humpback whale's current best strategy is to check its prey's location and encircle it, whereas WOA thinks its prey is a target. After reviewing the current best solution, the best search agent is considered. The different vehicle parameters, such as throughput, number of nearby vehicles, speed, velocity, distance, and speed, were used as the initial population during the start-up phase. The mathematical version of this operation is represented as follows. \[M=|V\cdot P^{*}(t)-X(t)| \tag{21}\]  
\[P(t+1)=P^{*}(t)-B\cdot M \tag{22}\]  
where \(t\) represents iteration, \(B\) and \(V\) are coefficient vectors, \(P^{*}\) is the position vector of best solution, and \(P\) is the position vector  
When determining the best solution, \(P^{*}\) of every iteration is periodically updated,  
\[B=2x*r-x \tag{23}\]  
\[V=2\cdot x \tag{24}\]  
Here, \(r\) is a random number which range from 0.1. During the exploitation and exploration phases, the range of the variable is reduced from 2 to 0. At the interval \([-1,1]\), the phase showed major differences. It was possible for the vehicle to move from its original position to a new position. In the 2D space of the interval \([-1,1]\), X and Y to X* and Y* were the possible achieved positions. Furthermore, the position was accomplished by updating the procedure, and mathematically represented as:  
\[M=|V\cdot P_{rand}-P| \tag{25}\]  
\[P(t+1)=P_{rand}-B\cdot M \tag{26}\]  
The spiral updating technique can be constructed to analyze the distance of vehicles with one other. This process is determined by the following equation:  
\[P(t+1)=M^{\prime}\cdot e^{sr}\cdot cos\ cos(2\pi r)+P^{*}(t) \tag{27}\]  
Here, \(M^{\prime}\) is the distance of one vehicle \(P(t)\) towards another vehicle \(P^{*}(t)\), \(t\) is the iteration, \(s\) denotes the shape of the logarithmic spiral, and r represents a random number in range \([-1,1]\). \[P(t+1)=\left\{P^{*}(t)-B\cdot V\ if\ p<0.5\ M^{\prime}\cdot e^{sr}\cdot cos\ cos(2\pi r)+P^{*}(t)\ if\ p>0.5\right\} \tag{28}\]  
To update vehicle position, 50% probability was chosen for the shrinking encircling mechanism or spiral model. Here, r denoted a random number between the range [0, 1]. To get the maximum optimal solution, the dragonfly optimization approach was hybridized with the whale optimization algorithm to allocate the bandwidth. The dragonfly's [33] population was initialized in terms of vehicle nodes of \(n_{j}\)\({}^{*}\).