## 6 Discussion
### Limitation Analysis and Future Research Direction  
The POM-PSASIDEA algorithm has significant advantages in terms of mission benefit, mission assignment rate, and mission response time when solving the MSIMPLTS problem. However, multi-satellite imaging mission planning in large-scale scenarios based on the POM-PSASIDEA algorithm still faces many challenges, which are mainly reflected in the following three aspects:  
1. Limitations of responsiveness in dynamic environments  
Although the MSIMPLTS-MLOO model and the progressive optimization mechanism proposed in this work can effectively cope with the complexity of large-scale mission planning, there are limitations in its ability to cope with the responsiveness in dynamic environments. In real-world applications, the execution environment of multi-satellite imaging mission planning is a complex environment with high dynamics and uncertainties. For example, when the observation requirements of imaging satellites change due to the interference of non-cooperating spacecraft, the emergency adjustment of satellite resources leads to conflicts in satellite resources; in-orbit operating satellites are affected by solar storms, space debris impacts, and other circumstances in the space environment, which cause the satellite attitude to lose control due to radiation from the electronic components and other situations, rendering the original plan partially ineffective, and so on. In highly dynamic environments, mission requirements may change frequently, or new tasks may arise unexpectedly, making it impossible for the current algorithms to respond efficiently to these changes in real time. Intelligent adaptive adjustment capability limitations  
Although POM-PSASIDEA can adaptively adjust based on the population size, which can improve the search efficiency and convergence speed of the algorithm, the adaptive mechanism of POM-PSASIDEA mainly relies on existing optimization frameworks, but it lacks the ability to effectively utilize the feedback from the task execution to carry out in-depth learning and self-optimization, and when coping with the dynamically changing large-scale task scenarios, the POM- PSASIDEA algorithm has limited ability to adaptively adjust to new task requirements that have not been encountered before. In addition, in complex mission scenarios, MSIMPLTS is affected by the diversity of mission resources and dynamic changes in the target mission, as well as spatial and temporal constraints, which often require a higher level of intelligent regulation mechanism, and it is difficult to make effective use of historical mission data to achieve autonomous optimization planning. Based on the above analysis, it can be seen that MSIMPLTS based on the POM-PSAIDEA algorithm still has limitations in terms of a fast response and intelligent adaptive adjustment in dynamic environments, and future research directions are further discussed below with respect to these limitations. Event-driven integration with MSIMPLTS  
MSIMPLTS is affected by unexpected events (e.g., urgent tasks or environmental changes) during the execution process, and the task requirements often change dynamically. The introduction of an event-driven task planning mechanism can enhance the flexibility and real-time responsiveness of task planning by presetting different types of event priorities to quickly respond to and prioritize urgent tasks. In addition, event-driven tasks can be combined with real-time data processing technology to dynamically adjust the task planning scheme through real-time monitoring of the task status and environmental changes to ensure that the task can be quickly responded to in unexpected situations. Therefore, the study of event-driven tasks combined with MSIMPLTS can ensure the flexibility and real-time performance of the task and achieve the optimal allocation of resources to the dynamically changing demands.