## 4 Performance Evaluation and Analysis
### Performance Analysis of GreenRP  
Figure 8a illustrates the iterative performance of the GreenRP algorithm, with the x-axis representing the number of iterations and the y-axis depicting the fitness of the  
Figure 6: Comparative chart of satellite residual energy. Figure 7: Lifecycle consumption curve of different algorithms. best individual in the current generation, indicating the level of convergence. To evaluate its convergence performance, the original genetic algorithm (using fixed crossover and mutation probabilities and a completely random initial population) was compared with GreenRP through three rounds of solution, ensuring fairness by using the same initial population samples for both algorithms in the first round. The SGA curve represents the iterative performance of the standard genetic algorithm, with the average of the evaluation metrics taken over three rounds of solving. GreenRP_x denotes the iterative performance of GreenRP in the x-th round of solving, illustrating GreenRP's self-learning capabilities with iteration curves corresponding to three rounds of solving. The simulation work was conducted under the condition of weight coefficients X:Y:Z = 1:1:1, where the three values represent the significance of network lifetime, packet loss rate, and delay in solving the ultimate objectives. It is observable from the figure that the iteration curves of the original genetic algorithm show significant fluctuations, occasionally exhibiting reverse growth, and tend to stabilize only in the later stages of iteration. GreenRP employs dynamic crossover and mutation probabilities, resulting in a pronounced decline in fitness trends and a smooth convergence process due to the implementation of an elitism preservation strategy. As the number of solving rounds increases, the starting point of the iteration curves significantly decreases, facilitating GreenRP's search for quality solutions and, thus, quicker termination of iterations. In this manner, the algorithm not only enhances the quality of solutions but also optimizes the use of computational resources, offering an effective strategy for solving complex optimization problems. In terms of algorithm complexity, GreenRP resolves 1440 snapshots corresponding to energy-saving topologies in a total of 7 h. Although the algorithm is complex, it is used for offline pre-computation, and the lengthy computation time does not affect its application. To investigate the impact of different weight ratios on algorithm performance, various weight ratios were selected for simulation testing. Additionally, to better understand the algorithm's convergence characteristics, performance data under the SP strategy was introduced in the figure as a baseline reference line. The SP algorithm does not take any energy-saving requirements into account and makes no changes to the topology. It only performs routing selection based on the shortest path. Moreover, the SP algorithm is widely accepted by the public and used in real-world scenarios. Figures 8b and 9, respectively, display the data on network lifetime, packet loss rate, and average service delay under different weight ratios. Figure 8: (**a**) Fitness iteration curve; (**b**) network lifetime metrics in relation to weight coefficients. Increasing a single coefficient improves the corresponding performance metric, while other metrics decline. For instance, increasing the delay weight coefficient (e.g., 1:1:9) or the packet loss weight coefficient (e.g., 9:1:1) results in a reduction in network lifetime compared to the baseline ratio of 1:1:1. Conversely, when the network lifetime coefficient is increased (e.g., 9:1:1), the enhancement in network lifetime is significantly pronounced. This is because the satellite optical network's energy-saving routing problem is a multi-objective optimization issue where different goals influence each other.