## 1 Introduction
Hence, an optimization algorithm that balances exploration and exploitation while avoiding local optima may solve application mapping problems for both small-scale and large-scale applications in an ideal manner. Metaheuristic algorithms use a mathematical model of social evolution to efficiently solve optimization problems by promoting high-level methods and local improvement strategies. Based on the factors and characteristics of the algorithm, an improved version of a nature-inspired meta-heuristic mapping technique is presented to achieve better results under allowed bandwidth limitations. The modified whale optimization algorithm (IWOA) features strong exploration and exploitation capabilities and a competitive convergence rate for a given set of problems. IWOA is proposed as the first step to finding an optimal solution to NoC application mapping, followed by implementing a hybrid IWOA featuring Improved Genetic Mechanism (IWOA-IGA). The genetic-based technique incorporates crossover and mutation to optimize IWOA-based results for optimal NoC mapping. The integrated tweaked GA features assist the IWOA in achieving the optimal mapping with faster convergence, allowing it to avoid local optima with enhanced search ability. These capabilities of the proposed algorithm make it superior to conventional techniques. To analyze the NoC efficiency metric, current research seeks to model power, latency, and communication cost. Successful mapping strategies generate optimum NoC mapping with low communication costs, fewer iterations, and fewer processing resources. X-Y routing simplifies mapping problems. In a regular mesh design, network performance is measured by the cost of communication:  
\[C_{T}=\sum_{i=1}^{size(ACG)}\sum_{j=0}^{te}[C_{B(te_{i})}]\times HopCount_{(te _{i})} \tag{1}\]where \(t_{e}\) represents a collection of edges within a core graph, \(B_{(t_{e_{i}})}\) represents the communication bandwidth on edge \(i\), and the number of hops that separate two communicating cores, referred to as \(HopCount_{(t_{e_{i}})}\), is used to quantify delay and communication costs [13; 14]. Thus, as suggested by Equation (1), lowering communication costs indirectly reduces latency and energy usage. IWOA-IGA offers an effective trade-off between performance measures and faster mapping onto 2D NoCs. The hybrid mechanism of the suggested method optimizes mapping results, reducing communication, energy, power, and latency costs. The contributions of the planned research are as follows:  
* Based on an improved whale framework incorporating GA characteristics, a novel and effective solution to NoC application mapping is proposed. * Improved Initial Mapping for IWO and direction-based crossover and mutation ability are introduced based on a ranked selection-based method during GA evolution. * The final mapping technique delivers superior performance in terms of total communication costs. The proposed IWOA-IGA improves power, energy, and communication costs compared to existing bio-inspired algorithms. The rest of this paper is arranged in the following manner: Section 2 presents the literature review; Section 3 discusses the mathematical formulation and performance metrics; Section 4 presents the framework of the proposed IWOA algorithm; Section 5 shows the enhanced version of the WOA algorithm featuring the improved genetic mechanism; our computational findings and analyses are presented in Section 6; Finally, Section 7 contains concluding remarks and discusses future research directions.