## 5 Iwoa-Iga--Enhanced WOA Algorithm Featuring Improved Genetic Mechanism
### Iwoa-Iga: Modified Iwo Algorithm Featuring IGA Characteristics
The proposed algorithm consistently achieves a significant average reduction in communication cost when applied to scenarios involving 128 cores, with respective improvements of 17.37%, 20.99%, and 16.12% over PSO, SA, and GA. These outcomes highlight that the proposed algorithm outperforms applications with a moderate number of cores. However, as the number of cores rises to surpass a certain threshold, the algorithm's performance becomes average compared to its efficiency with a lower number of cores. As the algorithm was further modified by adding an improved GA algorithm to ensure better results when larger applications are executed on it, the modified IWOA-IGA was tested again on larger TGFF-based applications implemented on 32, 64, and 128 cores. The IWOA-IGA minimizes communication costs effectively, with an average improvement of 45.66%, 50.56%, and 48.15%, respectively, for 32 cores. For the configuration with 64 cores, the average improvement in communication cost was 40.29%, 33.70%, and 41.33%, respectively. With 128 cores, the proposed technique produced respective average reductions in communication cost of 30.60%, 33.31%, and 26.67%. Table 5 shows the results. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Task Graph** & **Over PSO** & **Over SA** & **Over GA** \\ \hline
32 Cores & 41.31\% & 46.661\% & 44.00\% \\
64 Cores & 38.70\% & 31.94\% & 39.78\% \\
128 Cores & 17.37\% & 20.99\% & 16.12\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Percent communication cost savings with IWOA compared to other algorithms for extensive task graphs. 2.3 Performance Comparison of IWOA and IWOA-IGA: Optimizing Power and Energy in 2D NoC Benchmark Applications. To assess the proposed algorithm's power efficiency, we opted for the Orion Model [40; 46], which analyzes the power and energy of the network and can be integrated into a simulation environment to compute the network's overall energy. The NoCtweak simulator was used for power estimation, which was conducted using a standard cell library and the ORION-2 model, allowing for accurate evaluation of power consumption at different CMOS nodes. This simulation tool derives both power and energy consumption. By utilizing post-layout power details sourced from 2D NoC components, these estimations are based on component behavior when executing traffic patterns or real-time programs with a given mapping. Regarding the power metrics, the proposed IWOA improves power reduction by an average of 21.93%, 30.20%, 22.07%, 26.92%, 15.84%, 8.70%, 7.78%, 1.9%, 7.66%, and 5.48% over the PSO, SA, GA, ACO, BA, SCSO, ILP, iHPSO, CSO, and SFO algorithms, respectively. We observed that IWOA integrated with GA further improves the power reduction by an average of 22.09%, 30.34%, 22.23%, 27.07%, 16.24%, 8.89%, 7.97%, 2.07%, 7.84%, and 5.63% over the PSO, SA, GA, ACO, BA, SCSO, ILP, iHPSO, CSO, and SFO algorithms, respectively. It is notable that there was an average power difference of 3-4% when the simulation was carried out using the IWOA-IGA instead of the IWOA with real-time benchmarks. This is because both algorithms have different features and abilities to achieve optimal mapping solutions. Overall, these results demonstrate that the proposed algorithms consume less power than existing bio-inspired algorithms. The comparison of projected power values through the proposed methods is visually depicted in Figure 9.

Figure 10 shows the energy consumption for the various methods. The mapping technique implemented in the proposed work outshines competing state-of-the-art techniques such as PSO, SA, GA, and iHPSA in terms of energy consumption. In comparison to PSO, SA, GA, and iHPSA, IWOA reduces energy consumption by 1.7%, 13.89%, 17.47%, and 0.2%. The improved version yields improvements of over 3.22% compared to PSO, 15.38% compared to SA, 27.58% compared to GA, and 1.64% compared to iHPSA. Figure 8: Comparison of communication costs for large synthetic task graphs.