# A Hybrid Method for Mobile Agent Moving Trajectory Scheduling using ACO and PSO in WSNs
## 7 Performance Evaluation  
We first analyze the energy consumption of the network between different algorithms. As illustrated in Figure 9, as time rises, the energy consumption of the four algorithms increases, and LEACH rises more rapidly. We know that each of the four algorithms adopts a clustering algorithm. However, CHs are more unevenly distributed in LEACH. Some nodes may be far away from the CHs and that results in long-distance communication which consumes significantly more energy. HM-ACOPSO, VD-PSO, and ACO-TSP all introduce mobile data collectors for data gathering which conserve energy. The energy consumption of ACO-TSP increases most slowly before 5500 s because the mobile agent in ACO-TSP needs to access each CH and the transmission distance between the mobile agent and CHs is very close. Whereas, the CHs selection in ACO-TSP only considers the residual energy which causes significant energy dissipation in intra-cluster communication. VD-PSO takes energy balances into consideration so that it schedules a moving trajectory with much more energy consumption, but the energy of the whole network is much more balanced. Therefore, the energy increases steadily in VD-PSO. Our proposed HM-ACOPSO method features a more reasonable clustering method and the scheduled path is optimized so that it achieves better performance in aspects of energy consumption. We also research the traveling path that different algorithms schedule, and the result is described as Figure 10. We can clearly see from Figure 10 that HM-ACOPSO almost has the best performance in terms of scheduled path length. There is no doubt that the ACO-TSP achieves the worst performance in terms of traveling path length because the mobile agent in ACO-TSP needs to access each CH closely to collect information. VD-PSO schedules rendezvous points of mobile agents to sojourn for information gathering, and that is much more efficient compared to ACO-TSP. However, VD-PSO does not make full use of wireless communication range and it sacrifices some performance for balancing the energy of the whole network. One of the great improvements in HM-ACOPSO is that it makes full \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mathbf{\eta}\) & \(\mathbf{c_{1}}\) & \(\mathbf{c_{2}}\) & **Optimal Path Length** & **Iteration Number** \\ \hline
0.8 & 0.5 & 0.5 & 1015.9 & 33 \\
0.8 & 0.4 & 0.6 & 983.6 & 29 \\
0.8 & 0.3 & 0.7 & 1024.6 & 32 \\
0.8 & 0.6 & 0.4 & 1005.6 & 34 \\
0.7 & 0.4 & 0.6 & 1064.4 & 38 \\
0.9 & 0.4 & 0.6 & 1026.1 & 34 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Optimal path length under different PSO parameters combination. \begin{table}
\begin{tabular}{c c c c} \hline \hline \(\mathbf{\alpha}\) & \(\mathbf{\beta}\) & \(\mathbf{\rho}\) & **Optimal Path Length** & **Iteration Number** \\ \hline
0.5 & 0.5 & 0.5 & 2031.6 & 65 \\
0.5 & 1 & 0.5 & 1737.4 & 62 \\
1 & 1 & 0.5 & 1548.7 & 58 \\
1 & 2 & 0.5 & 1548.7 & 45 \\
1 & 3 & 0.5 & 1548.7 & 54 \\
2 & 1 & 0.5 & 1588.5 & 55 \\
1 & 2 & 0.4 & 1548.7 & 48 \\
1 & 2 use of the wireless communication range, and therefore, it can schedule the optimal traveling path for the mobile agent. We then study the package loss rate of each algorithm. We assume that all of the communication is successful and that the packages will be dropped only when the memory of the sensors overflows. The result is demonstrated as Figure 11. The package loss rate has great relevance to the efficiency of the mobile agent data gathering. In LEACH, the sink node is static, and all the data is transmitted to it timely by multiple-hop communication. Therefore, package loss will not occur. Whereas, in HM-ACOPSO, VD-PSO, and ACO-TSP, the mobile agent is introduced to collect data, and the shorter the traveling path and the less the sojourn points are, the more efficient the mobile agent is. Our proposed HM-ACOPSO method makes full use of the wireless communication range to shorten the mobile agent traveling path and merge the sojourn points to enhance the work efficiency of the mobile agent.