## 6 Experiments and Evaluation
In a hostile environment, the sensing error often exists in the applications of WSNs, so we consider it (\(r_{e}\)) in computing the probability of sensing the point of interest by one sensor node eventually used in the fitness function of MOCADMA. Figure 5 shows the average SCD _versus_ different values of \(r_{e}\) and indicates that the average SCD decreases with the increase of \(r_{e}\). It is clearly seen that the more uncertainty from the sensing error causes more difficulties for network coverage. Figure 3: Network lifetime _versus_ different values of \(\alpha\) with \(r_{e}=0\). Figure 2: The best dividing of virtual cells having maximum average SCD. Figure 6 presents the average SCD _versus_ network size with the fixed number of points of interest where \(\alpha=0.4\), \(r_{e}=0\). As Figure 6 shows, when 64 points of interest randomly and uniformly are deployed in the \(100\) m \(\times\)\(100\) m target area, and the number of deployed sensor nodes varies from 50 to 400, the average SCD increases with the increase of network size (\(n\)). Figure 4: Network lifetime _versus_ different values of \(\alpha\) with \(r_{e}=1\). Figure 5: Average SCD _versus_ different values of \(r_{e}\). In the next simulations, we verify the feasibility of MOCADMA in WSNs and consider 400 sensor nodes and 64 points of interest randomly and uniformly deployed in the 100 m \(\times\) 100 m target area. To evaluate the performance of the MOCADMA, an LEACH-based clustered routing protocol [26] is applied to the WSNs, and CoCMA [15], MA [18] and iMA [19] are re-implemented. In addition, the design of CoCMA, MA and iMA didn't allow the sensing error, so we only consider the WSNs with no sensing error (r = 0 ) in the simulation experiments. Figure 7 depicts the SCD _versus_ network lifetime in rounds (\(\alpha=0.4\) in MOCADMA). We note that all the algorithms have good capabilities in maintaining the sensing coverage, but MOCADMA has maintained the sensing coverage degree at 100% until the 3800 th additional round which is a significant improvement over other algorithms. As Figure 7 shows, the network lifetime is prolonged to 7000 more rounds by the MOCADMA, which lasts about 800 rounds longer compared to CoCMA and iMA. Figure 6: Average SCD _versus_ network size with the fixed number of points of interest. Figure 7: SCD _versus_ network lifetime in rounds. Figure 8 displays the percentage of node death _versus_ network lifetime in rounds ( \(\alpha=0.4\) in MOCADMA). The MOCADMA, CoCMA, iMA, and MA lose their 50% of nodes at the 5516 th, 5142 nd, 4889 th, and 4103 rd round, respectively. The simulation result demonstrates that the proposed MOCADMA significantly prolongs the lifetime of the network compared to other three methods. The proposed MOCADMA divides the WSNs at the early stage and helps form the clusters earlier than other three methods that save much energy, get the multi-objective Pareto-optimal solutions of network coverage by the fitness function presented in Equation (17) that has a quick-convergence characteristic, so the longer network lifetime can be obtained. The simulation results show that MOCADMA can achieve higher network coverage while improving the energy efficiency and effectively prolonging the network lifetime. Figure 8: Percentage of node death _versus_ network lifetime in rounds. Figure 9: Performances of the dynamic local search strategy in the different cases. Finally, we analyze the dynamic local search strategy of MOCOAMA through the simulation experiments with the same conditions as the previous experiments. In the normal case, there are tabu search algorithm, hill climbing search algorithm and adaptive directional local search strategy in the pool of local search algorithms, but when only one of those algorithms is in the pool, MOCOAMA would have a different performance. Figure 9 shows the performances of the dynamic local search strategy in the different pools, and the normal case of MOCOAMA has a better SCD than other cases.