# Large-Scale Multi-Objective Imaging Satellite Task Planning Algorithm for Vast Area Mapping
## 4 Experiment and Analysis
### Experimental Settings
#### 4.1.4 Parameter Settings for MOEAs  
In our experiments, NSGA-II, LMOCSO, and LMEA are comparative algorithms. Among them, NSGA-II is widely recognized as an MOEA with good performance for two-dimensional objective space when the decision variables of the problem are not particularly large. LMOCSO and LMEA are large-scale MOEAs based on new search strategy and decision variable grouping, respectively. The proposed ECL-INS-MOEA is a large-scale MOEA based on the new search strategy.  
For the parameter settings, the population size is always set to 100 for different regions and algorithms. The number of objective function evaluations is used as a termination condition of evolution, which is different for different regions. For each region, the maximum number of evaluations for NSGA-II, LMOCSO, and ECL-INS-LMOA is the same, while the maximum number of evaluations for LMEA is more than them because LMEA requires a large amount of decision variable correlation analysis. The evaluation numbers for different regions and algorithms are shown in Table 4. The other parameter settings of comparison algorithms are consistent with their paper. In ECL-INS-LMOA, the evolution process adjustment coefficient \(K\) is 0.6, the maximum flight time is 2, and the time adjustment coefficient is 0.7.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Region**} & \multirow{2}{*}{**Number of Decision Variables**} & \multicolumn{4}{c}{**Evaluation Times of Objective Function**} \\ \cline{3-6}  & & **NSGA-II** & **LMOCSO** & **LMEA** & **ECL-INS-LMOA** \\ \hline Congo (K) & 135 & 48,000 & 48,000 & 160,000 & 48,000 \\ India & 264 & 70,000 & 70,000 & 1,320,000 & 70,000 \\ Australia & 411 & 100,000 & 100,000 & 2,000,000 & 100,000 \\ The United States & 491 & 130,000 & 130,000 & 2,000,000 & 130,000 \\ Antarctica & 798 & 160,000 & 160,000 & 2,100,000 & 160,000 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Iteration termination evaluation times of each algorithm.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Region**} & \multirow{2}{*}{**Sub-Region**} & \multirow{2}{*}{**Imaging Time**} & \multicolumn{2}{c}{**Number of**} & **Number of Real/Binary** \\  & & & **Candidate Stripes** & **Decision Variables** \\ \hline \multirow{6}{*}{Australia} & 1 & 1 December 2019–31 December 2019 & 64 & \\  & 2 & 1 January 2020–31 January 2020 & 97 & \\  & 3 & 1 February 2020–29 February 2020 & 98 & 411 \\  & 4 & 1 March 2020–31 March 2020 & 105 & \\  & 5 & 1 April 2020–30 April 2020 & 47 & \\ \hline \multirow{6}{*}{The United States} & 1 & 1 December 2019–31 December 2019 & 115 & \\  & 2 & 1 January 2020–31 January 2020 & 131 & \\ \cline{1-1}  & 3 & 1 February 2020–29 February 2020 & 118 & 491 \\ \cline{1-1}  & 4 & 1 March 2020–31 March 2020 & 104 & \\ \cline{1-1}  & 5 & 1 April 2020–20 April 2020 & 23 & \\ \hline \multirow{6}{*}{Antarctica} & 1 & 19 December 2019–20 December 2019 & 20 & \\  & 2 & 20 December 2019–26 December 2019 & 96 & \\ \cline{1-1}  & 3 & 26 December 2019–1 January 2020 & 99 & \\ \cline{1-1}  & 4 & 1 January 2020–7 January 2020 & 91 & \\ \cline{1-1}  & 5 & 7 January 2020–13 January 2020 & 91 & 798 \\ \cline{1-1}  & 6 & 13 January 2020–23 January 2020 & 163 & \\ \cline{1-1}  & 7 & 23 January 2020–3 February 2020 & 79 & \\ \cline{1-1}  & 8 & 3 February 2020–16 February 2020 & 139 & \\ \cline{1-1}  & 9 & 16 February 2020–28 February 2020 & 20 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._