## 6 Methodology and Experimental Setup
After that, the algorithm performs the archive on the leaders and crowding operator on the elected leaders. The algorithm checks the state of the size of the leaders in which, if their size greater than the required size, the algorithm keeps the best leaders and eliminates the others. Hence, the velocity update rule comes to take place on the procedure, where is applied to each member of the population, after that, it performs the mutation operation. Moreover, the algorithm evaluates the objective functions (Equations from (8) to (19)), which uses the population members to minimize both the network end-to-end delay and end-to-end latency and also to maximize both energy efficiency and network throughput. In addition, the algorithm compares the new fitness of each individual with its old fitness value. The algorithm stores the new fitness just in case of the new one is better than the old. Then, the algorithm updates the leaders of the new generation of the population follows by archiving and crowding operators on the leaders. Finally, the algorithm checks the number of iterations. If the maximum generations (the value of 250 generations as in Table 9) is reached the algorithm will terminate, otherwise, the algorithm will repeat the past steps. Figure 4: The proposed network. \begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Parameter** & **Values** \\ \hline
0 & Time of interframe space (\(T_{ifb}\)) & 192 μs \\
1 & Transceiver’s transmitting to receiving turnaround time (\(T_{TA}\)) & 192 μs \\
2 & The duration of one backoff slot (\(T_{\textit{bosol}}\)) & 320 μs \\
3 & Use of ACKs & N0 \\
4 & PHY header (\(L\)_PHY_) & 6 bytes \\
5 & MAC header (\(L\)_MHR_) & 11 bytes \\
6 & MAC footer (\(L\)_MFR_) & 2 bytes \\
7 & The default minimum value of backoff exponent (\(macMinBE\)) & 3 \\
8 & The default maximum value of backoff exponent (\(a\)_MaxBE_) & 5 \\
9 & Number of sensors (\(n\)) & 5 \\
10 & Transceiver’s raw data rate (\(R_{data}\)) & 250 kbps \\
11 & The energy consumption in startup mode (\(E_{s}\)) & 8 mA \\
12 & Energy consumption through the communication (\(E_{c}\)) & 19.7 mA \\
13 & Sampling rate & 250 Hz \\
14 & Bit Error Rate (BER) & 0.0004 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Simulation parameters. Figure 5: Procedure of the OMOPSO algorithm. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **MOSFP** & **OMOPSO** & **NSGA-II** & **SPEA2** \\ \hline Population size & 20 & 20 & 20 \\ Archive size & (winner) 20 & 20 & (Elite) 20 & 20 \\ Mating pool size & - & - & - & 20 \\ Maximum generation & 250 & 250 & 250 & 250 \\ Crossover probability & - & - & 0.9 & 0.9 \\ Mutation probability & \multicolumn{3}{c}{\(1/\text{d}\) where d is the variable code size} \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Parameters of the algorithms. Figure 6: Procedure of the NSGA-II algorithm. Figure 7: Procedure of the SPEA2 algorithm. The procedure of NSGA-II, depicted in Figure 6, begins by initializing the packet payload size parameter. The lower limit and upper limit of this parameter are 0 byte and 114 bytes based on the IEEE 802.15.4 data frame, respectively. Based on the first population of this parameter, the algorithm evaluates the objective functions (Equations from (8) to (19)), which minimizes both the network end-to-end delay and end-to-end latency, and also maximizes both power efficiency, and network throughput. Moreover, the algorithm ranks the population based on non-dominated solutions and performs selection, crossover, and mutation operations to generate new population (child population). Based on the results of the previous steps, the algorithm uses the child population to evaluate the same objective functions. After that, the algorithm combines the child population with the old population. Later, it ranks the produced populations from the best to worst results. At the end, the algorithm checks on the number of iterations, in case it more than the maximum number of iterations (the value of 250 generations as in Table 9), the algorithm will terminate.