## 4 Results
### Simulation Results and Analysis  
Figure 7 illustrates the harvested power by the IoT device as a function of the distance between the source and the device, comparing various RIS phase-shift optimization methods when \(N=50\). Notably, 6 DChOA-DRL consistently outperforms all other approaches, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Proposed Methods** & **Parameters** & **Value** \\ \hline \multirow{8}{*}{6 DChOA-DRL} & Learning rate & 0.001 \\  & Discount factor (\(\gamma\)) & 0.95 \\  & \(\varepsilon\)-greedy & 0.21–0.93 \\  & Batch size & 128 \\  & Number of hidden layers & 6 \\  & Number of neurons per layer & 36 \\  & Activation function & Tanh \\  & Optimizer & 6 DChOA \\  & a & \([-1,1]\) \\
6 DChOA & f & Linearly from 2 to 0 \\  & Population size & 80 \\  & Iteration & 300 \\  & Number of hidden layers & 6 \\  & Number of neurons in hidden layers & 36 \\ DNN & Learning rate & 0.08 \\  & Activation & Tanh and sigmoid \\  & Optimizer & 6 DChOA \\  & Number of convolution layers & 6 \\  & Kernel size & \(3\times 3\) \\ CNN & Pooling type & Max pooling (2 \(\times\) 2) \\  & Number of neurons & 32 \\  & The inertial movement rate & 0.11 \\  & The movement toward the P\({}_{\text{best}}\) & 0.65 \\ PSO & The movement toward the G\({}_{\text{best}}\) & 0.93 \\  & Population size & 80 \\  & Iteration & 300 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameter setting of proposed methods through the trial-and-error method. indicating its superior ability to optimize the RIS phase shifts to maximize energy harvesting. As the distance increases, the harvested power decreases across all methods, which is expected due to increased path loss and signal attenuation over larger distances. However, the rate of decline differs across approaches, offering key insights. The DRL-based method with the 6 DChoA optimizer maintains the highest power even at larger distances, suggesting that the dynamic learning capability of reinforcement learning combined with the 6 DChOA's optimization strengths effectively compensates for signal degradation, highlighting the importance of using advanced optimization techniques that can dynamically adapt to varying wireless conditions, especially in scenarios where the RIS plays a critical role in extending coverage and improving energy efficiency in IoT networks. Figure 8 demonstrates the harvested power by the IoT device as a function of the distance between the source and the device, comparing different numbers of RIS elements (\(N=10,\ 20,\ 50,\ 100\)) using the 6 DChoA-DRL approach, along with a scenario without an RIS for reference. A key observation is that as the number of RIS elements increases, the harvested power consistently improves, with \(N=100\) providing the highest harvested power by the IoT device. This shows the clear advantage of using more RIS elements to enhance the energy harvesting efficiency, especially over longer distances. The distance between the source and the device causes the harvested power to decline in all cases due to increasing path loss. However, the presence of an RIS mitigates this drop more effectively as compared to the scenario without an RIS, where the harvested power plummets much more dramatically. This demonstrates how crucial the RIS is for extending the energy harvesting coverage area for IoT devices. Without an RIS, the harvested energy falls steeply as the distance increases, reaching values as low as \(-40\) dBm at a distance of \(15\) m. In contrast, with an RIS, even at the largest distance of \(15\) m, the harvested power remains significantly higher, showcasing the ability of the RIS to maintain feasible energy harvesting levels over a larger area. Figure 9 shows the achieved rate by the IoT receiver as a function of the source power for different numbers of RIS elements using the 6 DChOA-DRL-based optimization approach alongside the scenario without an RIS for comparison. As expected, the achievable rate increases with increasing source power for all configurations.