## 5 Iwoa-Iga--Enhanced WOA Algorithm Featuring Improved Genetic Mechanism
### Iwoa-Iga: Modified Iwo Algorithm Featuring IGA Characteristics
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Algorithm** & **VOPD** & **CALVC** & **MMS** & **MPEG4** & **MWD** & **MP3 Enc** & **263 enc** & **263 dec** & **PIP** \\ \hline ILP & 4119 & - & - & 3567 & 1120 & 17.021 & 230.407 & 19.823 & - \\ ONMAP & 4119 & - & 663,379 & 3567 & - & - & - & - & 640 \\ GA & 4141 & - & - & 3567 & 1321 & 17.133 & 230.69 & 19.911 & - \\ SA & 4125 & - & - & 3567 & 1451 & - & - & - & - \\ BEMAP & 4119 & 6701 & 664,636 & 3567 & - & - & - & - & 640 \\ ACO & - & - & - & 3670 & - & 17.231 & - & - & - \\ PSO & 4119 & - & - & 3567 & 1120 & 17.021 & 230.45 & 19.823 & - \\ BA & 4119 & - & - & 3567 & 1120 & 17.834 & 231.45 & 19.936 & - \\ HDPSO & 4119 & - & - & 3567 & - & - & - & - & - \\ CSO & 4119 & 6721 & 652,637 & 3567 & - & - & - & - & 640 \\ SCSO & 4119 & - & - & 3567 & 1122 & 17.021 & 230.407 & 19.823 & - \\ DPSO & 4119 & - & 688,297 & 3567 & 1120 & 17.021 & 19.823 & 640 \\ RAMAN & 4135 & - & - & 3774 & 1184 & 17.87 & 234.4 & 19.87 & 640 \\ SFOA & 4119 & - & - & 3567 & 1120 & 17.021 & 230.407 & 19.823 & - \\ ACA & 4119 & 6721 & 652,637 & 3567 & 1120 & 17.021 & - & - & - \\ iHPSA & 4119 & - & - & 3567 & 1120 & 17.021 & 230.407 & 19.823 & - \\ IWOA & 4119 & 6701 & 663,379 & 3567 & 1122 & 17.021 & 230.69 & 19.823 & - \\ IWOA-IGA & 4119 & 6701 & 663,379 & 3567 & 1122 & 17.021 & 230.69 & 19.82 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Communication cost (bw \(\times\) nh) comparison with real-world benchmark applications. #### 6.2.2 Performance Analysis on TGFF Random Instances

This section presents the details of the generated TGFF random instances and their use in evaluating the algorithms with varying mesh sizes. For the sake of experimentation with large-scale applications, random synthetic task graphs were generated using the TGFF tool, which generates task graphs with 32, 64, and 128 cores. TGFF, known as Task Graphs For Free, is meticulously crafted to offer a standardized approach for generating arbitrary task graphs for experimentation purposes in research work. In our evaluation, we used mesh sizes of \(6\times 6\) for up to 32 core placements, \(8\times 8\) to accommodate 64 core graphs, and 12 \(\times\) 12 for 128 core task graphs. The task graphs were configured to use a communication bandwidth of 50-600 MB/s between the interconnected tasks. As the tasks using the TGFF tools are randomly generated, there is no specific optimal value for the communication cost; however, there must be some criteria to evaluate the performance of the generated large-scale tasks. A few important considerations could include the number of generations for which the algorithms can execute and the communication cost value be achieved at that instance. Another consideration is the number of iterations for which the algorithm runs and the targeted communication cost to be achieved after a specified number of iterations. We observed that when the number of iterations was configured to a value between 180-200 and the IWAO-IGA was executed for the 200 iterations, the IWAO-IGA achieved the desired communication cost in fewer iterations than the IWOA for larger applications. This is due to the integration of directional crossover and mutation features into the IWAO mechanism, allowing the algorithm to perform better when searching for optimal solutions in the search space and to attain the desired cost in a lesser number of iterations than to the simple IWOA. Moreover, the proposed algorithm attains the desired cost within a reduced number of iterations in comparison with other advanced and optimization algorithms, e.g., SA, GA, and PSO. A cost savings-based comparative analysis is presented in Tables 4 and 5 and in Figure 8. Across the tested benchmarks, the proposed IWOA reliably lowers the communication cost by an average of 41.31%, 46.61%, and 44.00% compared to PSO, SA, and GA, respectively, for 32 cores. In the case with 64 cores, the average improvement in cost is 38.70%, 31.94%, and 39.78%, respectively.