# A Bee Colony-Based Optimized Searching Mechanism in the Internet of Things
## 3 Proposed Solution  
We present the enhanced ABC (E-ABC), in which the employed bee phase and onlooker bee phase of the ABC algorithm enhance the searching mechanism for better optimization. It presents a solution for the selection of the finest food site with higher quality than other algorithms. In order to obtain a faster convergence speed, the search schemas of the employed bees and onlooker bees are improved. Existing schemes [17; 33] also use a similar framework or structure. The list of notations is shown in Table 2. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Author** & **Article** & **Task** & **Algorithm Variant** \\ \hline \multirow{2}{*}{[PERSON] et al. [17]} & Honeybee swarm for & Numerical optimization search for & Standard ABC. \\  & numerical optimization & optimum solutions. & \\ \hline \multirow{4}{*}{[PERSON] et al. [21]} & A quick ABC (qABC) & \multirow{4}{*}{Adds new equation in onlooker \(\quad\)} \\  & algorithm and its & bee phase. & Utilizes standard ABC \\  & performance on & Enhances convergence speed. & with modified equations. \\ \hline \multirow{4}{*}{[PERSON] et al. [22]} & Improved quick artificial bee & Newly defines exploitation. \\  & colony (iqABC) algorithm & Improves early convergence rate with & Uses the ABC with four \\  & for global optimization & base solution. & new search schemas. \\ \hline \multirow{4}{*}{[PERSON] et al. [25]} & A modified artificial bee & Enhances convergence speed of the & Uses ABC algorithm \\  & colony algorithm for & standard ABC. & with frequency of \\  & real-parameter optimization & Improves efficiency for composite and & perturbation and \\  & non-separable functions. & modification rate. \\ \hline \multirow{4}{*}{[PERSON] et al. [26]} & Improved ABC for & Improves inverted generational distance & Uses the ABC with the \\  & multi-objective optimization. & and hypervolume values. & Two\_Arch2 method. \\ \hline \multirow{4}{*}{[PERSON]. [PERSON] et al. [28]} & Reliable data gathering in & \multirow{4}{*}{Presents a robust spanning tree in the IoT. \(\quad\)} \\  & the Internet of Things using & Improves stability and energy usage. & Generates spanning \\ \cline{1-1}  & artificial bee colony & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of existing ABC-based schemes. \begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Description** \\ \hline B & Blocks \\ \hline \(l_{i}\) and \(u_{i}\) & Lower and upper bounds \\ \hline \(x_{finest}\) & Finest food source \\ \hline \(x_{ij}\), \(v_{ij}\) & Old solution, new solution \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of notations. There are three phases, namely the employee bee phase, onlooker bee phase and replication position optimization phase, which describe the workflow of the proposed ABC, as shown in Figure 2. In the ABC algorithm, the search schemas rely on the exploitation and exploration abilities of bees to attain enhanced convergence rates. Prior to these modules, first, the food sites are initialized at random. Subsequently, the acquired food sites are examined and their values are estimated using the objective function. In the initial stage, control parameters such as the colony size, maximum iterations, maximum number and limit are initialized. Food sites are initiated at random, ranging between (0, 1), computed as \(x_{m,i}=l_{i}+\text{rand (0, 1)}\times(u_{i}-l_{i})\), where \(i\) denotes the total number of food sites set by scout bees and ranges from 1 to \(sn\). Moreover, \(m\) ranges between 1 and \(d\), where \(d\) represents the number of optimum parameters depicted so as to minimize the objective function. In addition to this, \(l_{i}\) and \(u_{i}\) represent the lower and upper bounds of the parameter \(x_{m,i}\), respectively.