## 5 Performance Evaluation and Comparison  
We compared the results obtained using the greedy algorithms (Greedy_MWM and Greedy_BM) with those obtained using the GA and DPSO algorithm in a set of simulation experiments. All experiments were performed using a program in C# on a NET platform. Specifically, the simulation was conducted using 40 distinct trajectories for each number of sensor spots, and each trajectory was applied 50 times. The recorded data were averaged to generate the final results. Table 1 presents the parameter settings in our simulations. Our simulations focused on determining algorithm performance regarding the effect of the number of sensor spots on the total energy consumption, the balance of the residual energy of all sensors, and the quality of the solutions. In terms of total energy consumption, system efficiency was evaluated according to the sensors' total traveled distance. Regarding the balance of residual energy, system efficiency was assessed by the differences in the distance traveled of all mobile sensors. Solution quality (and by extension, algorithm quality) was indicated by the fitness value. Figure 5 presents the effect of the number of sensor spots on the total traveled distance under the GA and DPSO algorithm, compared to the solutions obtained by the greedy algorithms. In terms of the total traveled distance, the greedy minimum weight matching algorithm performed the best regardless of the number of sensor spots, whereas the greedy bottleneck matching algorithm performed the worst. The DPSO approach performed slightly better than did the GA approach given the small number of sensor spots. However, as the number of sensor spots increased, the GA approach outperformed the DPSO approach by 0.1% to 0.3% in terms of the total traveled distance. This is attributable to the small number of sensor spots, to which the presence of many similar individuals in each population is ascribable. Therefore, the GA approach is more likely to approach a local optimum than is the DPSO approach. As the size of the problem increased, the GA saved the better results from the previous generation and passed them to the next generation through an evolutionary process. By contrast, under the DPSO approach, the better results in each generation did not always have the chance to improve the outcomes of each particle. Consequently, the GA approach was more likely to approach the global optimum through the evolutionary process. Figure 6 presents the effect of the number of sensor spots on the differences in the mobile sensors' residual energy under the greedy algorithms, GA, and DPSO algorithm. In terms of the total differences in the distance traveled by mobile sensors, the greedy approaches were considerably outperformed by the evolutionary schemes; their outcomes ranged from 33% worse to 53 times worse than those of the GA and DPSO algorithms. The GA approach outperformed the DPSO approach in terms of the balance of residual energy, regardless of the number of sensor spots. As the number of sensor spots increased, the difference between the performance of the GA and DPSO approaches increased from 2.1% to 77%. This is attributable to the small number of sensor spots, to which the presence of \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Meaning** \\ \hline \(A_{M}\) & 1000 m \(\times\) 1000 m & Network monitoring area \\ \(r\) & 25 m & Sensors’ coverage radius \\ \((x_{0},y_{0})\) & \((0,0)\) & Base station location \\ \(n\) & 20, 30, \(\dots\), 100 & Number of sensor spots \\ \(m\) & 5 & Number of mobile sensors \\ \(p\) & 100 & Population size \\ \(p_{c}\) & 0.8 & Probability of crossover (GA only) \\ \(p_{m}\) & 0.1 & Probability of mutation (GA only) \\ \(\omega\) & 0.1 & Probability of mutation (DPSO only) \\ \(c_{1}\) & 0.5 & Probability of crossover (DPSO only) \\ \(c_{2}\) & 0.5 & Probability of crossover (DPSO only) \\ \(l_{c}\) & 100, 1000 & Number of generations (GA) or iterations (DPSO) \\ \hline \hline \end{tabular}
\end{table}