## 5 Performance Evaluation  
We evaluated the results obtained with the DPSO and hybrid DPSO_GA algorithms in a set of experiments and compared the results with the results obtained with the greedy algorithm and GA. We performed all experiments with a program in C# on a Windows 10 computer. We investigated the effects of four network parameters on the number of covered targets and the number of activated sensors: the number of sensors \(N\), the number of targets \(M\), the field of view \(\theta\), and the sensing radius \(R\). In the experiments, all targets and sensors were randomly scattered in a region of size 800 m \(\times\) 800 m. Specifically, for each combination of network parameters, we randomly generated 40 instances of the network and reported the mean results. The simulation parameters are shown in Table 1.  
Our simulations focused on determining the algorithm's performance for various network parameters in terms of the total number of covered targets, the total number of activated sensors, and the quality of the solutions. The quality of a solution was represented by its fitness value. Figure 4 shows how the number of sensors has an impact on the target coverage rate, active sensor rate, and fitness values under the GA, DPSO, and DPSO_GA algorithms, compared with the solutions obtained by the greedy algorithm. This experiment had 200 targets, and number of sensors \(N\) was 50, 75, 100, 125, 150, 175, or 200; the sensor and targets were randomly generated in the specified region. The sensors' coverage radius \(R\) = 80 m, the field of view \(\theta\) = 60\({}^{\circ}\), and the weight of the fitness function \(w\) = 0.5. Figure 4a reveals that the greedy algorithm has at least a 17% higher target coverage rate than do the other three evolutionary algorithms. However, the active sensor rate of the greedy algorithm is also higher than others by at least 15% (Figure 4b). Therefore, in terms of the solution quality, the evolutionary algorithms achieve better performance than does the greedy algorithm by 0.01% to 40.08% (Figure 4c). Furthermore, among the proposed metaheuristic algorithms, the DPSO_GA algorithm outperforms both the GA and DPSO algorithms in terms of target coverage rate and fitness values; however, the DPSO_GA algorithm requires the greatest number of active sensors. The DPSO_GA algorithm outperforms the GA and DPSO algorithms by 0.4% to 2.9% in terms of the fitness value. Figure 4d plots the fitness values versus the number of generations for the GA, DPSO, and DPSO_GA schemes with \(N\) = 100, \(M\) = 200, \(R\) = 80, \(\theta\) = 60\({}^{\circ}\), \(w\) = 0.5, and \(lc\) = 1000. The DPSO_GA and DPSO approaches had higher fitness than GA did after 10 generations, and the DPSO_GA approach achieved solutions that were 0.42% and 0.82% better than those of DPSO and GA, respectively. Therefore, the proposed hybrid DPSO_GA algorithm can better identify high-fitness particles and transmit them to subsequent generations than the other algorithms. As a result, the hybrid scheme was more likely to achieve the global optimum solutions for the MCMRS problem in terms of the number of sensors. Figure 5 shows how the number of targets has an impact on the target coverage rate, active sensor rate, and fitness values for the GA, DPSO, and DPSO_GA algorithms compared with the solutions obtained with the greedy algorithm. In the experiment, the number of targets varied from 50 to 200, and the number of sensors \(N\) = 100. All targets and sensors are randomly generated in the specified region. The sensor coverage radius \(R\) = 80 m, the field of view \(\theta\) = 60\({}^{\circ}\), and the weight of fitness function \(w\) = 0.5. Figure 5a reveals that as the number of targets increases, the number of targets covered increases, but the target coverage rate decreases because increasing the number of targets increases both the number of uncovered and covered targets. The number of uncovered targets increases more quickly; thus, the total target coverage rate decreases.