## 5 Results
### Genetic Algorithm Convergence Analysis  
The convergence performance of the GA was evaluated by visualizing the evolution of key performance metrics--namely, the average revisit time, maximum revisit time, data transmission volume, and total number of satellites--across generations for different communication technologies. Figures 9-11 provide the generation-wise average fitness values (GA values) and the optimized fitness values (GA values) for satellite constellations obtained by combining different orbits (LEO and VLEO) and LPWAN technologies (NB-IoT, LoRa, and Mioty). These convergence graphs clearly illustrate the GA optimization process, demonstrating that the fitness values gradually stabilize as generations progress. This trend not only confirms the stability of the GA but also validates the effectiveness of the optimization procedure, thereby verifying that the algorithm has successfully derived an optimal satellite deployment configuration. Figure 9 illustrates the generation-wise variation in fitness during the satellite constellation optimization process for each communication technology. Best fitness converges almost entirely after generation 10, indicating that the genetic algorithm begins stabilizing around this point. Average fitness, while more volatile than best fitness, shows a gradual convergence trend after generation 14. The absence of average fitness values for generations 1-6 is due to excessively large values that exceed the visualization range. In the LoRa-based communication environment, best fitness converges after generation 8, whereas average fitness rapidly aligns with best fitness around generation 12, then stabilizes. For the Mioty-based environment, best fitness converges quickly in the initial generations and stabilizes after generation 13. Average fitness continues to decrease toward best fitness but without fully stabilizing. Figure 10 shows the generation-wise fitness variation for NB-IoT communication in the VLEO (300 km) environment, optimized using a genetic algorithm. Best fitness converges after generation 16, while average fitness gradually stabilizes after generation 12. Figure 9: GA convergence in the LEO environment for different communication technologies. In the LoRa-based environment (also shown in Figure 10), best fitness converges by generation 9, and average fitness stabilizes from generation 14 onward. Meanwhile, for Mioty in the same figure, best fitness converges rapidly after generation 4, with average fitness aligning closely by generation 12. Figure 11 shows the generation-wise fitness trend for NB-IoT communication in the VLEO (200 km) environment. Best fitness stabilizes around generation 7, while average fitness continues to converge, suggesting more generations may be needed for full convergence. In the LoRa-based environment (also shown in Figure 11), best fitness converges by generation 9, although average fitness remains slightly short of complete convergence. For Mioty, best fitness stabilizes rapidly by generation 11, whereas average fitness appears to converge but exhibits a slight upward trend. Across all three scenarios, best fitness values essentially converge, while average fitness, although not always fully converged, shows a clear downward trend. Consequently, we consider the constellation associated with the best fitness at generation 20 to represent the optimal configuration, indicating that each constellation was effectively optimized. Figure 11: GA convergence in the VLEO (200 km) environment for different communication technologies. Figure 10: GA convergence in the VLEO (300 km) environment for different communication technologies. Additionally, to further validate the economic efficiency and structural consistency of the optimized satellite constellation, we introduced several graphs (Figures 12-14) that illustrate the evolution of the satellite count corresponding to the best fitness values across generations.