## 5 Performance Evaluation  
According to the outcome of the simulations presented above, both the algorithms present satisfactory results of coverage and k-coverage of the area, which are highly close to the ideal values for each case, within acceptable execution times. A rather important factor which affects the quality of the solution as well as the computational execution time of the algorithm is the size of the population which is selected in the GA algorithm and the size of the swarm in the PSO algorithm. The choice of a large number of possible solutions, otherwise known as agents, exploring the search space increases the diversity, thus resulting in less iterations of the algorithm so as to converge to the optimal solution while at the same time the possibility of getting \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Values** & **PSO\_GRID [30]** & **PSO\_VORONOI [31]** & **GA** & **PSO** \\ \hline Mean Value & 91.74 & 89.83 & 96.40 & 95.53 \\ Standard Deviation & 0.01 & 0.01 & 0.59 & 0.66 \\ Average Time (sec) & 645.33 & 61.28 & 217.59 & 200.99 \\ p-value (GA) & 0.00 & 0.00 & - & 0.00 \\ p-value (PSO) & 0.00 & 0.00 & 0.00 & - \\ Ideal Area Coverage & & 100\% & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Simulation results of case study 5. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Values** & **PSO\_GRID [30]** & **PSO\_VORONOI [31]** & **GA** & **PSO** \\ \hline Mean Value & 61.07 & 59.24 & 62.50 & 62.38 \\ Standard Deviation & 0.00 & 0.00 & 0.23 & 0.18 \\ Average Time (sec) & 362.44 & 27.67 & 149.12 & 89.06 \\ p-value (GA) & 0.00 & 0.00 & - & 0.10 \\ p-value (PSO) & 0.00 & 0.00 & 0.10 & - \\ Ideal Area Coverage & & 62.83\% & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Simulation results of case study 6. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Values** & **PSO\_GRID [30]** & **PSO\_VORONOI [31]** & **GA** & **PSO** \\ \hline Mean Value & 97.89 & 97.96 & 99.76 & 99.55 \\ Standard Deviation & 0.02 & 0.01 & 0.10 & 0.17 \\ Average Time (sec) & 125.18 & 27.17 & 39.24 & 44.98 \\ p-value (GA) & 0.00 & 0.00 & - & 0.00 \\ p-value (PSO) & 0.00 & 0.00 & 0.00 & - \\ Ideal Area Coverage & & 100\% & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Simulation results of case study 7.  
trapped in local extremes decreases. Contrastively, the selection of a large population size concerning the GA algorithm as well as a large swarm size of particles as far as the PSO algorithm is concerned, increases to a great extent the complexity of the calculations. During the use of both of the methods, large sizes for the population and the swarm were chosen, since the need for finding the optimal coverage and k-coverage result was prioritized compared to the fast execution of the algorithms. Additionally, it was observed that when the complexity of the problem increased, while maintaining the sizes of population in GA algorithm and swarm in PSO algorithm, the standard deviation in the simulation results was higher. Thus, while the difficulty of the problem increased, larger sizes of the populations were chosen. The fact that was noticeable, was the ability of the PSO algorithm to adapt and solve the most demanding problems, where the requirement of k-coverage was added, having a smaller swarm size compared to the GA algorithm where the respective population size was not sufficient to find a solution to the coverage problem. Nevertheless, for comparison purposes equal population sizes were chosen in all case studies examined. As far as the mean value of the coverage calculation is concerned, the GA algorithm outperforms the PSO algorithm in all cases except for case study 4, where PSO produces a far superior result. The differences between the two algorithms were found to be statistically significant with a confidence level higher than 99% in all case studies, with the exception of case study 6, where the confidence level dropped to 90%.