# An Optimized Load Balancing Using Firefly Algorithm in Flying Ad-Hoc Network
## 3 Proposed Network Model  
### Problem Formulation  
There are different categories of load balancing as follows:  
* Load balancing using software-defined networking (SDN),
* User Datagram Protocol (UDP),
* Transmission Control Protocol (TCP),
* Server load balancing (SLB),
* Virtual load balancing, multi-site load balancing, and elastic load balancing, also known as global server load balancing (GSLB), and
* Geographic load balancing.  
Geographic load balancing reallocates user traffic among data centers in multiple locations for maximum efficiency and security [21]. GPMOR is proposed as a method for determining the best available next hop to effectively reduce the impact of network latency caused by highly dynamic mobility. To begin, we used the Gauss-Markov mobility model to predict node position to reduce routing failure. Second, we used the deployment relationship to more precisely select the next hop for routing. We looked at the firefly algorithm, which has three major aspects that are relevant to the optimization technique. The firefly algorithm is simple and efficient. The benefits of such computation are that they are usually efficient for specific problems, require a small number of rounds, and are capable of dealing with highly nonlinear, multimodal optimization problems naturally and efficiently. The firefly algorithm has a very fast convergence rate in terms of the probability of finding the globally-optimized answer. It does not employ velocities and does not necessitate a good initial solution to begin its iterative process. It can be combined with other optimization techniques to create hybrid tools. Internal load balancing takes place inside a centralized environment, whereas geographic load balancing takes place across numerous sites. The mathematical notations are described in Table 2.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Work** & **EED** & **PDR** & **Fuel Emission** & **Throughput** \\ \hline [PERSON] et al. [8] & ✓ & ✗ & ✗ & ✗ \\ \hline [PERSON] et al. [9] & ✓ & ✗ & ✗ & ✓ \\ \hline [PERSON] et al. [10] & ✓ & ✓ & ✗ & ✗ \\ \hline [PERSON] et al. [11] & ✓ & ✓ & ✗ & ✓ \\ \hline [PERSON] et al. [12] & ✓ & ✗ & ✗ & ✓ \\ \hline [PERSON] et al. [13] & ✓ & ✗ & ✗ & ✓ \\ \hline [PERSON] et al. [14] & ✓ & ✓ & ✗ & ✓ \\ \hline [PERSON] et al. [15] & ✓ & ✗ & ✗ & ✗ \\ \hline [PERSON] et al. [16] & ✓ & ✗ & ✗ & ✓ \\ \hline [PERSON] et al. [17] & ✗ & ✓ & ✗ & ✗ \\ \hline [PERSON] et al. [18] & ✗ & ✓ & ✗ & ✓ \\ \hline [PERSON] et al. [19] & ✓ & ✗ & ✗ & ✓ \\ \hline [PERSON] et al. [20] & ✓ & ✓ & ✗ & ✓ \\ \hline Proposed work & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Traffic congestion control parameters of FANET.  
Assume that each flying node can achieve function \(fn(r)\) by creating a packet flow rate of \(r\), where \(fn(r)=\omega\log\left(r\right)\) and \(\omega\) is a constant. This effort purposes at exploiting the overall function of all the flying nodes in the network under the node-link capacity of the network and total delay along with the multiple flying nodes. Hence, the problem can be formulated as a function \(F_{1}\), which is defined as below:  
\[F_{1}=max\sum_{s}^{E}fn(r) \tag{1}\]  
Here, the optimization problem \(F_{1}\) is the main problem of optimization of flying nodes in the network. Here, the Equation (2) is defined as below:  
\[\sum_{s}^{S}r\leq\mathrm{Cn} \tag{2}\]  
\[\sum_{\mathrm{Li}}^{\mathrm{Ls}}Dn\leq\theta \tag{3}\]