## 4 Proposed Framework of IWO Algorithm
### Proposed IWOA  
In this section, a new and improved metaheuristic IWOA is presented that has powerful local and global search functionality. Each search agent that constitutes the mapping solution is initialized using a certain approach between the minimum and maximum limit in the range. In the proposed technique, the fitness function is often a primary goal to be reduced. The proposed algorithm commences with a collection of solutions achieved using the enhanced initial mapping mechanism instead of the conventional random mappings. At each iteration, search agents that represent individual mapping solutions adjust their positions concerning either a randomly chosen search agent or the current optimal solution with the minimum communication cost. The number of iterations is predefined, and varies depending on the application. Each mapping solution \(S\) is in the solution space \(SS\), where \(S\epsilon\)\(SS\) represents its components such that \(n>0\) components, i.e., \(S=S_{1},S_{2},.....S_{n}\), where \(i=1,2,3,.....,n\); here, n represents the scope of the optimization problem to be solved. To promote better exploration and exploitation, the value of parameter a is varied from \(2\) to \(0\). Random mapping is chosen in the proposed mapping technique when \(\mid A\mid>1\). In contrast, the selection of the best candidate mapping is carried out when \(\mid A\mid<1\) for modifying the mapping solutions of other search agents within the search space. This adaptive variation-based feature of the vector \(\vec{A}\) permits the IWOA to transition seamlessly between exploration and exploitation while achieving better mapping results. After performing in-depth analysis and comparison, the appropriate values for the input parameters of the algorithm are determined. It should be noted that a parameter combination that works well for one situation may not work the same way for another. Algorithm 2 provides  
Figure 4: IWOA Flowchart. the pseudocode of the IWOA. In each iteration, the algorithm efficiently obtains a better mapping solution as compared to the previous iterations, depending upon the fitness function. The termination criteria is the achievement of the required fitness value, that is, when the mapping solution with the lowest communication cost has been obtained. The IWOA's method for solving the optimization issue is a global optimizer which includes exploration and exploitation capabilities. As the algorithm runs, the initial mappings are fine-tuned to adjust the placement of tasks to the cores, ensuring that better solutions are achieved in each iteration. The flowchart of IWOA is presented in Figure 4.  
```
Initialization: Input:\(enhancedInitMapping,taskGraph,itrWOA,itrIGA,a,C,l,A,p\) stopCriteria:\(itrTotal,OptimalCommCost\) // Find pop, v, fitness \(initPopFitness(enhancedInitMapping,pop_{s}ize,meshSize)\) // Find gbestpop, gbestfitness \(getinitbest(fitness,pop)\) //\(X^{*}=BestSearchAgent\) while\(stopCriteria\)do for\(i\gets 1\) to \(itrWOA\)do for\(searchAgent\gets 1\) to \(pop_{s}ize\)do  Update parameters \(A\), \(C\), \(a\), \(l\), \(p\) \(\vec{A}=2\vec{a}.\vec{r}-\vec{a}\) \(\vec{C}=2\vec{r}\) if\(p<0.5\)then if\(|A|<1\)then  Update the current mapping solution using Equation (24), finally updating \(pop[j]\) endif if\(|A|\geq 1\)then  Select a random mapping solution-Xrand  Update the position of the current mapping solution by the Equation (32), finally updating \(pop[j]\) endif endif if\(p\geq 0.5\)then  Update the position of the current mapping solution by the Equation (28)  updating \(pop[j]\) endif endfor // Check If a search agent-based solution exceeds the search space, modify it.