# Large-Scale Multi-Objective Imaging Satellite Task Planning Algorithm for Vast Area Mapping
## 4 Experiment and Analysis
### Results and Analysis  
#### 4.2.1 Verification of Particle Update Strategies in ECLUS  
To demonstrate the effectiveness of ECL-INS-LMOA, we verify the effectiveness of the proposed particle update strategies through ablation experiments. The improved loser update strategy, flight time, and binary decision variable update strategy BCSO were compared with existing particle update strategies. The winner selection strategy based on SDE has not been validated because its effectiveness was studied in the literature [45]. Figures 7-9 show the Congo (K) planning results of the proposed ECL-INS-LMOA with different strategies under 40,000 evaluations. Figure 7 shows the impacts of the improved loser update strategy in ECLUS and the loser update strategy in LMCCSO on the performance of ECL-INS-LMOA. Figure 8 shows the flight time strategy in ECLUS and the fixed flight time strategy on the performance of ECL-INS-LMOA. Figure 9 shows the BCSO strategy in ECLUS and the BPSO strategy on the performance of ECL-INS-LMOA. In Figures 7-9, the red lines represent the results of the strategies in ECLUS, and the dotted lines of blue, green, and magenta represent the results of the comparison strategies.  
In Figure 7, the hypervolume (HV) is used to evaluate the performance of the improved loser update strategy in ECLUS and the loser update strategy in LMOCSO. The larger the HV, the better the convergence and distribution of the planning results. Therefore, from Figure 7, it can be seen that the improved loser update strategy in ECLUS has a better impact on ECL-INS-LMOA than the loser update strategy in LMOCSO. Figure 7 shows that the improved loser update strategy in ECLUS has a better maximum coverage than the loser update strategy in LMOCSO. However, even if the number of imaging strips of the improved loser update strategy in ECLUS has a downward trend, Figure 7 shows that it is still inferior to the loser update strategy in LMOCSO. This is probably because the maximum coverage obtained by the loser update strategy in LMOCSO is small, resulting in a small number of corresponding imaging strips. In general, the improved loser update strategy in ECLUS is better than the loser update strategy in LMOCSO because the improved loser update strategy in ECLUS has advantages in HV and maximum coverage.  
Figure 8 compares the flight time strategy and fixed flight time strategy from the HV, maximum coverage, and the number of imaging strips corresponding to the maximum coverage. Figure 8 shows that the strategy with flight time always achieves better results than the strategy with fixed flight time. It demonstrates the effectiveness of adopting a flight time strategy in ECLUS.  
From Figure 9, it can be seen that the binary decision variable update strategy BCSO exhibits a better performance than BPSO in ECL-INS-LMOA. Although the maximum coverage advantage of BCSO in Figure 9 is small, the HV advantage in Figure 9 is marked. This is mainly because BCSO obtains better convergence and distribution binary solutions than BPSO. Figure 9 also demonstrates the effectiveness of BCSO in the number of imaging strips corresponding to the maximum coverage.  
Therefore, the improved loser update strategy, the flight time strategy, and the BCSO strategy can effectively enhance the optimization ability of ECL-INS-LMOA. These three proposed strategies, together with the winner selection strategy based on SDE, ensure the excellent optimization performance of ECL-INS-LMOA in solving large-scale multi-objective optimization problems.