## 4 Results and Discussions
### Performance Analysis  
Here, the proposed framework's performance is analyzed and compared with the existing works. * Performance analysis for system parameter optimization: Evaluation of the performance of the proposed 2-EHRIO was carried out against the existing Elk Herd Optimizer (EHO) [27], which is a swarm-based metaheuristic inspired by the elk's natural breeding behavior, which includes two separate seasons: rutting (mating competition) and calving (offspring dispersal). During the rutting season, the population was divided into families led by dominating \"bulls\", with fitter solutions forming larger harems, encouraging intensification in high-quality areas. During the calving season, \"offspring\" solutions were distributed randomly throughout the search space, increasing variation. At each iteration, the EHO switched between these two phases--family formation and offspring dispersal--using simple separation and regrouping operators to maintain a dynamic balance of exploration and exploitation. Shark Smell Optimization (SSO) [28] treats each potential solution as a \"shark\" that detects an abstract \"odor\" field determined by the objective function, simulating a shark's olfactory hunting. In each iteration, sharks conduct a rotational local search using sampling points surrounding their new locations to select prospective regions, following the performance of a smell-driven global search in which they update their positions and velocities in the direction of the increasing odor concentration. SSO can effectively navigate complicated, multimodal landscapes with very simple updating algorithms thanks to key control factors that manage to balance between exploration and exploitation, such as the rotation radius, forward step size, and scent sensitivity coefficient. Another approach using a population-based metaheuristic similar to the predatory tendencies of antlions is called the Ant Lion Optimizer (ALO) [29]. Within a search space, each candidate solution (referred to as a \"ant\") conducts a stochastic random walk. Selected elite solutions (referred to as \"antlions\") efficiently trap and direct these walks by establishing dynamic probabilistic limits. Ants explore globally in each iteration by accruing random steps that are bounded by the current \"trap\" of an antlion. The search is then concentrated locally by updating antlion placements based on the fittest ants found. ALO is well-suited for adjusting Continuous-Variable QKD parameters, such as modulation variance, reconciliation efficiency, and privacy-amplification settings, to maximize the secret-key rate under realistic channel conditions, in view of its alternating exploration-exploitation scheme, which strikes a balance between intensification and diversification. The Crow Search Algorithm (CSA) [30] is a population-based metaheuristic built on the modes hiding food by error, followed by finding it again. Each \"crow\" in the community is used as a possible solution and remembers where it hid the food. Crows explore the search space by taking random walks that are affected by two important factors: flight length (step size) and awareness probability (chance of being followed). The crow improves its own memory when it finds a better place to hide (food cache). This maintains the balance between intensification (by taking advantage of known good positions) and diversification (by exploring at random). This easy-to-use but effective system makes CSA a good choice for fine-tuning CV-QKD parameters like modulation variance, reconciliation efficiency, and privacy-amplification settings, to obtain the highest secret-key rates in real-world channel conditions. Figure 3a,b illustrate the key rate, Secret Key Capacity, Error Correction, and Privacy Amplification analysis of both the proposed and existing methodologies for system PO. The suggested 2-EHRIO attained a key rate and secret key capacity of 1000 bits per second (bps) and 950 bps, respectively, demonstrating superior performance relative to the existing approaches, as illustrated in Figure 3a.