## 3 Multi-Objective Optimization Algorithms
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Author** & **Algorithms** & **Features** & **Study/Findings** & **Limitations** \\ \hline [PERSON] et al. [6] & \multirow{4}{*}{ECCA} & Maximize network coverage \& minimize the number of sensor nodes and the consumption & The algorithm was tested by changing the number of sensor nodes and the topology sizes. & Model of end-to-end \\ \cline{1-2} \cline{4-4} [PERSON] et al. & \multirow{4}{*}{MBPSO, BPSO, GA} & Maximize network lifetime \& minimize task execution time & MBPSO outperformed the other algorithms in term of optimizing the proposed features. & The features of network coverage and throughput are not evaluated. \\ \hline [PERSON] et al. [8] & \multirow{2}{*}{PSO} & Maximize network coverage & The best coverage for the area of 50 \(\times\) 50 is when the number of nodes is equal to 40 nodes. & The end-to-end delay and energy \\ \hline [PERSON] et al. & \multirow{2}{*}{OGDC, NSGA-II} & Maximize network coverage \& minimize used 210 nodes to cover the topology while OGDC requires 327 nodes to cover the same topology area. & Other features such as network throughput are not discussed. \\ \hline [PERSON] et al. [10] & \multirow{2}{*}{NSGA-II, SPEA2} & Maximize network coverage \& minimize & Model of end-to-end \\ \cline{1-2} \cline{4-4} [PERSON] et al. [10] & & Maximize network coverage \& minimize & NSGA-II outperformed SPEA2. & end-to-end delay \\ \cline{1-2} \cline{4-4} [PERSON] et al. [11] & \multirow{2}{*}{MOEA/D, NSGA-II} & Maximize network coverage \& minimize & MOEA/D outperformed NSGA-II in random the optimal results of the proposed objective functions. & The node selection problem of WSN is not discussed. \\ \hline [PERSON] et al. [12] & \multirow{2}{*}{GA, BPSO, CSP} & Node selection problem to achieve minimum energy consumption. & BPSO outperformed other algorithms in finding the optimal number of selected sensors. & Effect of node selection problem on network delay is not studied. \\ \hline [PERSON] et al. & \multirow{2}{*}{Improved version of MOPSO, MOPSO, MOPSO, common} & Maximize network coverage \& minimize the improved MOPSO outperformed the original MOPSO in maximizing the network coverage and minimizing the network energy consumption & Features that are affected by the interference resources are not examined. \\ \hline [PERSON] et al. [14] & \multirow{2}{*}{NSGA-II, MOEA/D} & Maximize network coverage \& minimize & NSGA-II outperformed MOEA/D in minimizing the energy consumption and maximizing the node coverage. & Optimizing end-to-end delay model is not discussed. \\ \hline [PERSON] et al. [15] & \multirow{2}{*}{NSGA-II, MOPSO, SPEA2} & Maximize packet throughput, energy efficiency \& minimize & NSGA-II outperformed both SPEA2 and OMOPSO in optimizing the proposed features. & Network end-to-end delay is not evaluated. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison between state of arts. simulates the motility of sperm swarm through the fertilization procedure [37]. SSO is inherently continuous technique of updating the position and velocity of each sperm on search space domain until reaching the optimal solution [19; 37]. Due to the wide variety of optimization problems that need a solution at low cost in short time, many researchers have extended these algorithms to solve different kinds of multi-objective problem. Therefore, we propose to apply three optimization algorithms to determine the optimal solution of a set of features that affect the QoS of any WSN. These algorithms are OMOPSO [16], NSGA-II [17], and SPEA2 [18]. In addition, we use our multi-objective version of SSO algorithm, called MOSFET algorithm for this purpose [19]. The selection of these algorithms was not arbitrary, which study in [38] finds that OMOPSO is the most commonly use algorithm among the swarm intelligence algorithms. This is because OMOPSO has a higher quality of results and performance.