## 5 Iwoa-Iga--Enhanced WOA Algorithm Featuring Improved Genetic Mechanism
### Iwoa-Iga: Modified Iwo Algorithm Featuring IGA Characteristics
As \(Nav_{C}\) is directly proportional to \(Perf_{CF}\), a higher \(Nav_{C}\) value for an algorithm indicates better performance in terms of average convergence for a specified number of runs. The calculated \(Perf_{CF}\) values for PSO, SA, GA, IWOA, and IWOA-IGA are 0.71, 0.68, 0.74, 0.76, and 0.82 respectively, which shows that the proposed algorithms converged several more times compared to the others when applied to the real and synthetic task graphs. Figure 12 shows the graphical representation of the convergence factor results as derived using \(Nav_{C}\). The graph shows the better values of \(Nav_{C}\) and performance based on the convergence factor achieved by the proposed algorithms in comparison to other competitive algorithms. A statistical analysis of the results reveals that the IWOA-IGA hybrid algorithm significantly reduces communication costs across different core configurations. For 32 cores, the hybrid algorithm achieves an average reduction of 48.12% compared to the other \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Applications** & \(Nav_{c}\) & \(NR_{total}\) & \(Perf_{cf}\) \\ \hline PSO & 10.71 & 15 & 7.14 \\ SA & 10.28 & 15 & 6.85 \\ GA & 11.14 & 15 & 7.42 \\ IWOA & 11.42 & 15 & 7.61 \\ IWOA-IGA & 12.42 & 15 & 8.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance evaluation based on convergence. optimization algorithms. Similarly, for 64 cores the reduction is 38.11%, while for 128 cores it is 30.53%. Regarding energy efficiency, the algorithm outperforms PSO by 3.22%, SA by 15.38%, GA by 27.58%, and iHPSA by 1.64%. Additionally, the IWOA-IGA exhibits superior convergence, with a \(Perf_{CF}\) value of 0.82, indicating its effectiveness on both real and synthetic task graphs. The IWOA-IGA shows improved latency as well, achieving improvements of 2.87% over PSO, 6.08% over SA, 6.61% over GA, and 2% over iHPSA. In conclusion, the improved whale optimization algorithm integrated with specialized modified genetic algorithm properties presents superior performance in solving mapping problems within Network-on-Chip (NoC) architectures. Its effectiveness surpasses that of other state-of-the-art algorithms, establishing it as a viable and advantageous solution for addressing complex NoC challenges. ## 7 Conclusions

In this paper, we have presented a hybrid model incorporating an improved whale optimization algorithm with a modified enhanced version of genetic algorithm for the allocation of real-world applications onto 2D NoCs. The proposed algorithm incorporates enhanced initial mapping instead of random initial mapping to provide a head start to the optimization algorithm in achieving the global optimum solution. In the first step, the IWOA algorithm is introduced to address the application mapping challenge. To further improve the efficiency of the proposed algorithm, an enhanced and modified genetic algorithm which uses expert-based selection, direction-based cross-over, and mutation abilities is integrated with the IWOA to produce high-quality mapping solutions. This tweaked GA features helps the IWOA to achieve optimal mapping with faster convergence, allowing it to avoid local optima through its enhanced search capability. Extensive experimentation and analysis were performed with both real-time benchmarks and synthetic large-scale task graphs. The proposed IWOA-IGA shows significant improvements regarding communication cost, average power, energy, and latency over other competitive algorithms, demonstrating its high potential. In future work, the proposed algorithm can be employed to map real-time applications onto alternative NoC topologies. Conceptualization, S.S., F.H. and N.K.B. ; methodology, S.S.; implementation, S.S.; validation, S.S., F.H. and N.K.B. ; formal analysis, S.S.; investigation, S.S., F.H. and N.K.B. ; data curation, S.S.; writing--original draft preparation, S.S.; writing--review and editing, F.H. and N.K.B. ; supervision, F.H. and N.K.B. ; project administration, N.K.B. All authors have read and agreed to the published version of the manuscript.