# Large-Scale Multi-Objective Imaging Satellite Task Planning Algorithm for Vast Area Mapping
## 5 Discussion  
This paper focuses on the research of large-scale multi-objective imaging satellite task planning in large areas, where large areas generally refer to areas with more than 100 imaging strips. On the one hand, it is easy to solve imaging satellite task planning in small areas using existing MOEAs. On the other hand, if the large area is decomposed into several small areas for optimization, only the local optimal solution can be obtained, limiting the acquisition of the optimal solution. We should strive to explore better imaging solutions to utilize precious satellite resources and complete imaging tasks more efficiently. The proposed ECL-INS-LMOA achieved better optimization results than the existing MOEAs. This is because, firstly, ECL-INS-LMOA performs global optimization by directly designing more efficient search strategies and better environmental selection strategies. The global optimal solution of ECL-INS-LMOA is preserved. However, the MOEAs based on the decision variable grouping method and the decision space reduction lose the global optimal solution because of the methods of processing decision variables. Secondly, the proposed ECLUS has a faster convergence speed than the strategy in LMOCSO. The particles go directly from position \(x_{l}(t)\) to position \(x_{l}(t+2)\) with one evolution. On the other hand, ECLUS adjusts the flight distance by introducing flight time to accelerate convergence and avoid oscillation convergence. In addition, BCSO was proposed for updating the binary decision variables. Thirdly, ECL-INS-LMOA adopts the idea of two-stage optimization. In the early stages of evolution, ECLUS and NSGA-II-SDE run alternately, which focuses on fast convergence while balancing global optimization. In the later stage of evolution, only NSGA-II-SDE runs, which focuses on global optimization while balancing fast convergence. ECL-INS-LMOA not only provides the optimal planning solution, but also has advantages in runtime. Firstly, ECL-INS-LMOA is based on a new search strategy without the need for additional analysis and the calculation of decision variables, so the time consumption is significantly less than LMEA. Secondly, the accelerated particle update strategy, flight time, and the winner selection strategy in ECLUS are all linear, and their increased computational cost is negligible and can be ignored. Compared with ECL-INS-LMOA, only non-dominated solutions in LMOCSO are used in particle updates per generation, which results in LMOCSO having many more evolutionary generations than ECL-INS-LMOA within the same number of objective function evaluations. As a result, the environment selection strategy of LMOCSO is executed more frequently, resulting in a lower running efficiency than the proposed ECL-INS-LMOA. Thirdly, all particles in ECL-INS-LMOA participate in winner and loser selection rather than just non-dominant particles. On the one hand, it can maintain the diversity of solutions. On the other hand, ECLUS can select winner individuals by simply comparing SDEs, and then obtain new populations by only updating the losers. In contrast, NSGA-II requires a series of operations such as competitive selection, crossover, and mutation on all individuals to obtain new populations. Therefore, ECL-INS-LMOA has a higher running efficiency than NSGA-II. In our study, based on previous research, we specifically defined the scope of a large area, which is the area with more than 100 imaging strips. And a large-scale multi-objective optimization algorithm was specifically designed to solve large-scale task planning problems efficiently. We rarely see these in other related studies [46; 47]. Although the imaging area is large in some studies, the scale of their problems may not be large. It is believed that this paper can provide some new references for large-scale satellite mission planning problems.