# An On-Orbit Task-Offloading Strategy Based on Satellite Edge Computing
## 4 Algorithm Introduction and Simulation Analysis
### Simulation Analysis  
This research first created a 6 \(\times\) 5 = 30 LEO satellite network based on the Iridium NEXT architecture. STK was used to obtain the shortest distance between satellites in each time slot in order to obtain the route table \(SatTEG\). The specific scene parameters [19; 21] and the related parameters of the GABPSO algorithm were set as shown in Table 2. In this paper, we provide the following reference algorithms for comparison to confirm the effectiveness of the proposed approach. * Binary particle swarm algorithm (BPSO): [PERSON] et al. proposed a similar offloading problem in the satellite edge computing scenario and used the BPSO algorithm to provide a solution [21]. * Genetic algorithm (GA): Genetic algorithms are frequently utilized to resolve computational offloading issues in the ground cloud edge computing scenarios [34; 35]. * Modified particle swarm algorithm (MPSO): [PERSON] et al. proposed using the MPSO algorithm to solve the task-offloading problem of formation-flying satellites [19]. The authors inverted the flight velocity of some particles with a certain probability and perform position updates. The \"variant particles\" were created to obtain better search performance. The same parameters were set equally throughout the algorithms to avoid losing generality, and the scene parameters were identical. Meanwhile, the relevant simulation results were averaged over 30 runs to reduce the impact of stochasticity. Since the optimization objective of this study is the system cost obtained by weighting the delay and energy consumption, different combinations of weights will be studied first. The energy consumed during offloading is far greater than the time delay. When \(\alpha\) is 1 and \(\beta\) is 0.1, the two are nearly equal. Therefore, we will keep \(\alpha\) at 1 and explore the impact on the system cost as \(\beta\) grows. As illustrated in Figure 4, the energy consumption had an increasing impact on the system cost as \(\beta\) rose, and the system cost grew gradually. However, the GABPSO algorithm was able to solve for the lowest system cost whether the delay and energy consumption were essentially equal or the energy consumption was given much more importance than the delay. In subsequent simulations, \(\alpha\) was set to 1 and \(\beta\) was set to 0.1 to simulate the scenarios where delay and energy consumption were equally important. We first chose to explore the convergence performance of the four algorithms. It is noteworthy that we simulated two representative scenarios in which the high-load satellite ratios were 20% (30 \(\times\) 20% = 6) and 60% (30 \(\times\) 60% = 18), respectively. Most of the solutions in the search domain were defined as infeasible solutions when there was a high percentage of high-load satellites. When the number of high-load satellites is small, it will not have much impact. It was necessary to perform corresponding simulations to explore the possible consequences. The simulation figures demonstrate that the four algorithms performed nearly identically for two different conditions. In the process of particle evolution of the BPSO and MPSO algorithm, there were individual historical best position \(P_{xbest}\) and the global best position \(G_{xbest}\) of the particle population controlling the direction of the optimal solution. Therefore, compared with the simple GA, the BPSO and MPSO algorithms could move more quickly toward the optimal solution. These two algorithms, however, reached local optimality, while the particle swarm diversity vanished. The GABPSO algorithm combined the benefits of the PSO algorithm's quick convergence and the GA algorithm's