## 4 Proposed Framework of IWO Algorithm
### Inspiration for NoC Application Mapping  
The metaheuristic method described in [36] simulates hunting behavior using a random agent or the best search agent to track prey. Researchers have found that Humpback Whales pursue prey by creating distinctive bubbles in a circular pattern, and are the only species known to engage in bubble-net feeding [37; 38]. The hunting mechanism of whales is modeled in the proposed work to carry out NoC application mapping optimization. The whales' hunting mechanism uses search agents to find the optimal position. As the search agent approaches the prey, the other agents update their locations to discover the best solution. Each search agent corresponds to the task mapping solution, which is assessed on the basis of communication cost. Based on the reference mapping solution, other mapping solutions update their locations (tasks to core mapping). The basic WOA algorithm employs random initial mapping solutions, which are improved over further WOA iteration. However, in this work we modify the WOA algorithm to generate an initial mapping solution using a set of criteria, providing the optimization algorithm with a head start. The algorithm then progressively improves the initial mapping through further iteration. Modified WOA with Targeted Initial Mapping Generation Heuristic searches for IP mapping are frequently employed; however, they often become trapped in local optima. This shortcoming is primarily attributable to the first population/solution generation stage of heuristic searches. Most transformational heuristics generate a random starting population, and the search procedure can then be seen as a search of the entire search space. In some heuristic procedures, the initial population/solution is formed using predefined criteria, expert information, and/or system attributes. These criteria may not work for large or complex problems. If the initial population generation is not effective, incremental improvement towards finding an optimal solution will be limited. In swarm-based intelligence optimization techniques, the initial population quality has a significant impact on the algorithm's speed and accuracy [39; 40]. In the proposed work, the WOA algorithm is modified in terms of initial mapping generation, and is then employed to enhance overall application mapping to NoC Cores. The initial population is produced using the good point set approach instead of the random initial population generation in the standard WOA. This method is an efficient approach that can aid in minimizing the number of attempts and reaching optimal solutions in fewer iterations. The solutions produced using the good point set sequence exhibit a better distribution of solutions than the sequences chosen by the general random method [41]. This approach is used to generate initial mapping solutions for mesh-based 2D NoC architectures. To generate a mapping sequence using the good point set method, a sequence of m points in s-dimensional space is generated and represented as discussed below: Generate points such that  
\[r=r_{1},r_{2},r_{3}, \tag{18}\]  
\[r_{i}=2\times Cos(2i/p), \tag{19}\]  
where \(i\) can be set to any value for generating various mapping points, and let \(p\) be a prime which satisfies \(p\)\(\geq\)\(2s\)\(+\)\(3\). Now, using the several mapping points generated, a mapping sequence is generated that is stored in \(T_{(s-map)}\):  
\[T_{(s-map)}=(r_{1}\times k),(r_{1}\times k),(r_{1}\times k),....(r_{m}\times k) \tag{20}\]  
where \(r_{1}-r_{m}\) are the total mapping points generated and \(k\)\(=\)\(1\), \(2\), \(3\),..., n, where n is designated as the total number of cores for which mapping is generated. For NoCs havingcertain application tasks to be mapped onto s \(\times\) s sized mesh architectures, we can take m as the number of generated tasks or points.