## 6 Discussion
### Discussion of the Current Research  
1. Scalability of MSIMPLTS based on Multi-layer Objective Optimization  
The MSIMPLTS model based on Multi-layer Objective Optimization (MSIMPLTS-MLOO) provides strong scalability and adaptability by setting up an upper, middle, and lower layer structure to handle different optimization objectives in layers. Specifically, the MSIMPLTS-MLOO model is relatively independent among the layers and can flexibly cope with MSIMPLTS problems with different target task sizes through the adaptive adjustment of optimization objectives and constraints in each layer. In addition, the hierarchical processing of the model effectively reduces the complexity of the problem, and the layer-by-layer optimization enables the objective function and constraints of each layer to be optimized in a more precise range, thus avoiding the computational complexity and local optimal solution problems that may occur in global optimization. Through the simulation experiments of the MSIMPLTS-MLOO model, it can be seen that the MSIMPLTS-MLOO model is able to maintain a fast response with the increase in the number of satellites and targets when processing MSIMPLTS, and it also has excellent performance in terms of the mission benefit.  
2. Effectiveness of the progressive optimization mechanism  
The construction of a progressive optimization mechanism makes the mission planning process more orderly and systematic. In the allocation optimization phase, the allocation sequences of satellites and target tasks are generated by considering the objective function of maximizing the mission revenue, so that effective allocation of satellite resources can be achieved. Then, in the time window optimization phase, according to the decision scheme in the allocation optimization phase, a feasible observation time window is assigned to each target mission with the objective function of maximizing the mission allocation rate so as to reduce the possibility of mission conflict. Finally, minimizing the response time objective function is further considered in the global optimization phase, which integrates the decision results of the previous two phases to output a task planning scheme adapted to the current environment. Simulation experiments show that the progressive optimization mechanism, which divides the optimization process into three phases, enables the algorithm to gradually refine the planning scheme and thus more dynamically adjust for different optimization objectives in each phase, improving the overall quality of the planning scheme and the practical application effect and making it adaptable to task scenarios of different scales and complexities.  
Superiority of the population size adaptive strategy for an improved differential evolution algorithm  
The construction of a population size adaptive strategy for an improved differential evolution algorithm has significant advantages in solving the MSIMPLTS problem. The PSASIDEA algorithm adaptively adjusts the population size according to the algorithm's optimization search process, which reduces the consumption of computational resources while ensuring the global search capability. In addition, the adaptive adjustment of the population size prevents the algorithm from falling into the local optimum prematurely, which can effectively deal with the complexity of MSIMPLTS. The simulation results show that the PSASIDEA algorithm can significantly improve the mission benefit and mission completion and effectively shorten the convergence time when dealing with the multi-satellite imaging mission planning problem in large-scale scenarios. The population size adaptive strategy of the algorithm performs particularly well in solving the problems of premature convergence and local optimality that exist in traditional differential evolutionary algorithms.