## 4 Results
### Experimental Results and Analysis  
The pareto fronts (PFs) of reserved non-dominated solutions for the test cases with low, medium, and high loads are shown in Figures 5-7, respectively. The \(x\)-axis and \(y\)-axis represent the additive inverse of the profit and the perturbation. We can easily find that, in all test scenarios, the MODMSA outperforms other multi-objective comparison algorithms in terms of the diversity and the quality of non-dominated solutions. It is obvious that the pareto fronts (PFs) of the MODMSA are well distributed in both the \(x\)-axis and \(y\)-axis, whereas the non-dominated solutions obtained by other comparison algorithms are centrally distributed, which proves the effectiveness of our proposed multi-objective optimization algorithm in improving the diversity of non-dominated solutions. Especially in test scenarios with a heavy load, the non-dominated solutions obtained by other comparison algorithms are almost the same as each other. In addition, it is worth noting that only the MODMSA can obtain non-dominated solutions with different perturbations, and the non-dominated solution obtained by other comparison algorithms are concentrated in areas with high perturbation. This is because the evolutionary operators such as crossover and mutation introduce large perturbation and the perturbations are accumulated continuously in these multi-objective comparison algorithm. Therefore, the MODMSA is promising for the practical EOS dynamic mission-scheduling problem as the MODMSA can provide more high-quality candidate observation plans with various perturbations for the decision-makers of the EOS system. Figure 5: Non-dominated solutions reserved in different test cases of DMP-PP with 400 static missions. Figure 6: Non-dominated solutions reserved in different test cases of DMP-PP with 600 static missions. Figure 7: Non-dominated solutions reserved in different test cases of DMP-PP with 800 static missions. The optimal values of each objective obtained by all multi-objective scheduling algorithms in different test cases are listed in Tables 2 and 3. The MODMSA can simultaneously obtain the maximum profit and the minimum perturbation in all the test cases. Regarding the profit rates, the gap in the maximum profit rates between the MODMSA and other multi-objective comparison algorithms increases significantly with the the number of missions increases. Compared to comparative algorithms, the MODMSA improved by at least 2.42%, 8.40%, and 14.09% on average in the test cases with low, medium, and high load, respectively. The superior performance of our proposed algorithm in observation profit is due to the fact that the proposed evolutionary operators and profit-oriented local search algorithm can fully utilize the heuristic features of the DMP-PP. Moreover, I-MOMA and D-MOMA are superior to the other three classical multi-objective comparison algorithms, which indicates the advantage of the memetic-based multi-objective scheduling algorithm in solving the complex multi-objective observation-mission-scheduling problem. In terms of the perturbation, the MODMSA can obtain the non-dominated solution without perturbation, whereas the minimum perturbations of other comparison algorithms are much higher than the MODMSA. This is because the perturbations generated in evolutionary operators, such as crossover and mutation, are unavoidable and accumulated,, whereas the non-dominated solutions generated at each iteration of the MODMSA are preserved in the archive set and do not participate in subsequent iterations.