## 5 Discussion  
In this section, we analyze the results obtained from our proposed 6 DChOA-DRL-based optimization framework and interpret them in the context of existing studies and methodologies. The findings are evaluated against state-of-the-art approaches, including RL, a CNN, and PSO, to highlight the advancements introduced by our work in enhancing energy harvesting and communication efficiency in RIS-assisted IoT networks. The discussion focuses on how the proposed framework effectively addresses the dual objectives of maximizing the harvested power and maintaining a high QoS, as hypothesized, while achieving significant performance gains. By systematically comparing our results with  
Figure 10: The convergence curve of methods in (a) epoch = 180; (b) epoch = 240; and (c) epoch = 300.  
previous studies, we provide a comprehensive understanding of the contributions and limitations of the proposed method, establishing its relevance and superiority in the field. Table 2 shows the performance comparison of various methods for optimizing RIS phase shifts in IoT networks. The results clearly demonstrate the superior performance of the proposed 6 DChoA-DRL compared to other benchmark methods. The RMSE of 6 DChoA-DRL is significantly lower at 0.13, compared to higher values such as 3.34 for RL and 6.91 for the CNN, indicating a much more precise optimization of RIS phase shifts. Additionally, the MAPE of 1.24% further supports the accuracy of the 6 DChoA-DRL algorithm, suggesting that the prediction error is minimal compared to methods like the 6 DChoA (7.96%) and ChOA (14.60%). These metrics confirm that the hyper-parameter optimization provided by the 6 DChoA leads to better convergence and lower prediction errors in complex environments, ensuring the RIS configuration is highly effective in enhancing signal strength and data transmission rates. Furthermore, the SD of 0.27 for 6 DChoA-DRL reflects the stability and consistency of the algorithm, with much less variability compared to other approaches like the DNN (11.43) or PSO (20.07). Additionally, the mean time required for 6 DChoA-DRL to complete the optimization task is also competitive at 486 s, outperforming more computationally expensive methods like the ChOA and PSO. This efficiency, combined with high accuracy and stability, showcases the strength of 6 DChoA-DRL in delivering optimal RIS configurations. Table 3 shows the comparison of the run time of various algorithms according to different RMSE termination conditions for optimizing RIS phase shifts. The results highlight the significant efficiency of the proposed 6 DChoA-DRL in terms of the run time under various RMSE termination conditions. As shown, 6 DChoA-DRL consistently outperforms all other methods, reaching an RMSE of <15.00 in just 59 s, while other methods, such as RL and PSO, take 156 and 529 s, respectively. The same trend continues for more stringent conditions, with 6 DChoA-DRL achieving an RMSE < 10.00 in 148 s and an RMSE < 5.00 in 209 s, compared to 221 s and 406 s for RL, with no other methods reaching these thresholds. This indicates that the 6 DChoA-DRL algorithm converges faster and more efficiently, particularly when accuracy is critical. The fact that 6 DChoA-DRL reaches an RMSE < 2.50 in 318 s, while no other algorithms achieve this level, showcases the superior convergence capability of the proposed method. This rapid convergence directly relates to the effective hyper-parameter tuning provided by the 6 DChoA, which fine-tunes the DRL to optimize RIS phase shifts efficiently. The proposed 6 DChoA-DRL framework demonstrates significant improvements in energy harvesting and communication performance for RIS-assisted IoT networks. However, its implementation is not without challenges. One key implication is the increased computational complexity introduced by the 6 DChoA, which, while effective in optimizing the RIS phase shifts and hyper-parameters, demands higher processing power and longer convergence times compared to traditional approaches.